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2017 December 20

Rachel Labianca, Pharmacy resident

  • "I previously attended biostatistics clinic in August in preparation for VICTR grant application for my residency research project on time to antibiotics and open fracture trauma patients. I am in the process of collecting data during December, and would like to attend clinic again to ensure that I am formatting the data most appropriately to be ready for statistical analysis. I also would like to determine more specific statistical endpoints to guide my data collection now that I will a definitive number of patients and event rates."

Gary Owen, Pharmacy resident

  • "Follow up from 11/29 re: assessing pain, agitation, and delirium practices in an international cohort. Would like to solidify a plan for VICTR application."

2017 December 13

Timothy Olszewski, Research dietitian

  • "Dr. Heidi J Silver is the PI on an IRB-approved retrospective study on sex differences in sarcopenia surgical outcomes. We would like to discuss data that we have analyzed."
  • The cohort of interest includes subjects with surgery for benign tumors or malignant tumors. The analysis will separate out these two groups. If someone had a benign surgery and also a malignant surgery, a covariate indicating prior surgery may be in order. Or only the first surgery should be included. The number of subjects who fall into this category need to be determined. For subjects who had >= 2 surgeries in one cohort or the other (ie., benign or malignant), only the first surgery should be considered.
  • Several outcomes are of interest -- dichotomous ones such as ED revisit, rehospitalization, death will be modeled using logistic regression; time to event outcomes will be modeled using Cox proportional hazard models, provided the proportional hazard model assumptions are met.
  • For all models, clinical knowledge should guide the choice of potential confounders to include in the model.
  • This project should fit in a standard 90-hour VICTR voucher. approved.

Carsen Petersen, Registered dietitian, MS student

  • "I have completed data collection and now need to start data analysis. Dr. Heidi Silver is my mentor on the IRB-approved retrospective study. We are examining the agreement between common prediction equations for estimation of resting energy expenditure (REE) and measured REE using the KORR ReeVue indirect calorimeter in free-living obese adults. Statistical analyses will be completed to understand if age, sex, race, or obesity grade influences the agreement."
  • The first question of interest is the agreement between the measured REE and several already established predictions of calories expended in a day. We discussed the use of Bland-Altman plots to illustrate agreement as well as using different colored points to illustrated the relationship for different sub-groups in their cohort (e.g., men/women, race).
  • In order to assess the relationship between the different factors of interest (e.g., BMI) and measured REE, a linear model with mREE as the outcome and BMI fit with restricted cubic splines could be fit (adjusting for any potential confounders). Plots of mREE by BMI would best illustrate this relationship.
  • This project should fit in a standard 90-hour VICTR voucher. approved.

2017 December 6

Kaitlyn Works, Emergency Medicine

  • "QI for the waiting room: gauging patient expectations before and after implementation of an informative video."
  • Make answer options as clear as possible (eg, if patient would say they expect to wait 2 hours, make options clear about where 2 hours would go)
  • Timing of survey: watch video and take survey after triage; need to think about how to get survey to patients who might go immediately from triage to treatment room, or come in via ambulance
  • Discussed specific changes to survey made during clinic
  • Possibility: X months of baseline data collection; X months of "passive" intervention (play video at intervals in waiting room, patient treatment rooms); X months of "active" intervention (sending patients to a specific room to watch video after triage, eg)

2017 November 29

Gary Owen, Pharmacy/Critical Care Resident

  • "We are using a large, international database of critically ill patients (collected from the international study of mechanical ventilation) to study how clinical practice compares to pain, agitation, delirium guideline recommendations. Our objectives, planned analyses, and questions are below:
  • (1) Objective: Describe current practice and changes between 2010 and 2016 (before and after 2013 guidelines) Key variables (measured daily): RASS scores, amount of sedation/analgesia, daily spontaneous awakenings, occurrence of delirium Analysis: Descriptive stats for years 2010 and 2016, pertaining to amount of analgesia/sedation, depth of sedation, spontaneous awakening, along with outcome of delirium. May compare groups before/after guideline implementation in 2013, as well." Question: options for describing adherence (e.g. how to quantify daily spontaneous awakenings for the group - average, frequency, per pt, etc.). dealing with missing data considering any delirium during admission or day-to-day delirium
  • (2) Objective: Determining which aspects of clinical practice are associated with delirium Key Variables: daily measures as above; facility, region/nation; pt specific factors Analysis: Multivariate logistic regression of delirium vs available covariates. Question: how to consider presence of delirium - any delirium during admission vs daily risk
  • (3) Objective: Identifying factors associated with deviations from guidelines. Key variables: year, location, patient specific Analysis: Multivariate analysis. Question: How to quantify adherence (all or none during admission, day to day, each component of guidelines)."
  • Prospective data collection for a month at each site, first intubation only for all intubated patients
  • First goal: describe changes in clinical practice. Can do this well for sedation/analgesia practices; RASS was only collected in 2016 so can't really look at change for this. SATs may be mandated as part of the study protocol, so much harder to determine whether usual care changed between the two time points (since these practices are prescribed by the study). For medications, most reasonable to describe daily doses (not totals over intubation).
  • Delirium is collected as daily yes/no (no ICDSC/CAM-ICU available). 2016 data has about 25,000 patients (can have >1 day each); if logistic regression is used, limiting sample size = minimum of [delirium, no delirium]; divide this by 10-20 to get number of covariates you can include reliably.
  • Fixed vs random effects: Use a fixed effect if you want to make inferences about a covariate (eg, relationship between age and delirium); use random effects if you want to account for variability (eg, between study sites) without making inferences.
  • Multinomial regression may be a better choice than logistic regression - this looks like separating normal vs delirium vs coma, instead of delirium vs [anything else]
  • Data are clustered by both patient and site; it'll be important to adjust for this (could use sandwich estimation, bootstrapping, and/or random effects)
  • Key questions for next clinic: 1) What descriptives are both most interesting and most available/reliable in dataset? 2) Do you really want to look at adherence risk factors? - this will take lots of time due to need to define "adherence," could be confounded, etc; 3) Logistic (delirium vs anything else) or multinomial (n/d/c, but more complicated) for delirium outcome

2017 November 15

Bradley Kook, Obstetric Anesthesia Fellow

  • "We aim to perform a retrospective chart review assessing variability in timing of nurse administered PRN opioids in post-cesarean patients."
  • Lots of value in a descriptive analysis here, once you have ability to classify patients together
  • Could look at intraclass correlation between nurses and patients...?
  • Can easily get lots of data from EMR
  • Would be interesting to look at years of nursing experience, possibly
  • Defining outcomes is hard
  • Planning to get a VICTR voucher; may come back to help refine outcomes and analysis plan once more data is collected

2017 November 8

Andrew Perez, Medical Student

  • "We are looking at rates of complication-related port removal when patients are neutropenic vs normopenic at the time of port placement."
  • Data already collected on cases with clinically defined neutropenia, including some demographics, whether port was removed due to infection, whether 60-day followup obtained, and reason for incomplete followup (including death)
  • Main question today: how many controls to collect data on (SD is not tenable for this situation - no ready-made variable in SD that says "port removed due to infection")
  • No data available currently on controls other than year of port placement (ie, can't stratify on much); make sure that cases and controls represent roughly the same time frames
  • Other, preferred option if feasible: Collect data on all controls, so you don't have to choose a random sample, and use neutrophil as a continuous exposure as opposed to a case/control dichotomous variable
  • To help with feasibility, could restrict data (exposure, outcome, confounders) to, say, last five years; risk losing power because you lose events, but you're still gaining some by including neutrophils as continuous; probably still net gain
  • Clinical messaging: think about a figure showing neutrophil value on the X axis and probability of port removal on the Y axis - sends a concise message, whatever the relationship ends up being (recommend modeling with restricted cubic splines)

2017 November 1

Rachel Coleman, Endocrinology Fellow

  • "Review of patients behavior regarding their insulin pumps, specifically looking at how their bolus behaviors (for example: how manual bolus events and bolus wizard events affect their a1c). Have data for 224 pump downloads (two weeks of data for each of 224 patients) in a de-identified excel file as well as R file. I would like to discuss which statistical analysis is best and how to run the statistical analysis."
  • Key question is how the manual vs wizard users are different - manual users are probably very different, have been using the pump and doing manual calculations for a long time
  • If more a1c measurements were available, probably want to do some kind of longitudinal analysis - a1c (measured every 3 or 6 months) over time vs manual vs wizard, or a continually varying measure of override vs wizard, since patients can do both
  • If stick to single download per patient that corresponds to one measured a1c, maybe look at % of overrides over previous two weeks vs a1c at that visit; if you stick with the two groups, try various levels of cutoffs vs sticking with only 90%
  • Important to take potential confounders into account: there may be factors that affect both whether the patient decides to override the wizard and the a1c (eg, patients with disease for longer may be better at managing their a1c and more likely to manually override at times). Examples - time since diagnosis, comorbidities, age, etc

Shayan Rakhit, Medical student

  • "The Sequential Organ Failure Assessment (SOFA) score, is commonly used to dynamically evaluate a patientís severity of illness over the course of their ICU stay and contains six components measuring six organ systems. The neurologic SOFA component is the Glasgow Coma Scale (GCS), but the GCS has high inter-rater variability and is not routinely collected in ICUs. For this reason, Vasilevskis et al published (at Vanderbilt in 2016) a validation of a SOFA score utilizing the Richmond Agitation Sedation Scale (RASS), a much more reliable and reported measure of consciousness. Specifically, construct validity was determined with correlation with regular SOFA score and predictive validity was determined with regards to mortality (compared to regular SOFA score).
  • We have access to Respira/4th ISMV, a multi-center cohort across 42 countries. Our aim is to 1) validate (as done previously) the modified SOFA score utilizing RASS against the original SOFA utilizing GCS in this larger, more diverse population; and 2) evaluate if specific patient characteristics, such as direct neurologic injury, and practices, such as light sedation, affect the validity of a modified RASS utilizing RASS."
  • High rates of missingness, which are probably informative - patients without RASS/GCS are likely to be different
  • Recommend adjusting for site (or country, if site is untenable), in main or sensitivity analysis
  • Data is pretty clean
  • This project should fit in a standard 90-hour VICTR voucher. approved.

2017 October 18

Paula Smith, Surgery

*" I am using a large multi-institutional retrospective database to look at the relationship between insurance status and oncologic outcomes in Gastrointestinal Neuroendocrine Tumors. I have dome some preliminary work with this data but would like assistance from a biostatistician gaining more sophisticated understanding of my data."
  • Outcomes of interest include time to mortality and time to disease relapse.
  • Given that multiple sites are involved, fitting Cox models stratified by site would be most appropriate.
  • For the disease relapse outcome, we discussed treating death as a competing risk and calculating cumulative incidence.
  • She would like to apply for a VICTR voucher; the scope of work, along with any manuscript revisions, will fit easily into the 90 hour time frame.

Tanya Marvi, Medical student

*"We wrote a paper looking at factors associated with increased blood loss in pediatric scoliosis surgery. We are looking to address some fo the reviews from the journal regarding our statistical analysis as we prepare to resubmit the manuscript."
  • The primary concerns were lack of details regarding how the model was determined as well as whether any transformation of the data were necessary to meet linear model assumptions. Step-wise methods were originally used; we discussed how this is not the best approach and how to go back and redo the analysis using clinical knowledge/literature to determine which covariates to include. We also discussed how to check if model assumptions are met and if not how to address.
 

2017 October 11

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Konrad Sarosiek, Plastic Surgery

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Konrad Sarosiek, Plastic Surgery -- Cancelled

 
  • "We have a large data set of ~60,000 patients who underwent different surgical procedure combinations under the guise of Ďmommy makeoverí and we are looking to see if there is added risk when combining procedures. We are looking to find relative risk & to isolate frequent complications & identify risk factors."
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Andrew Perez, Medical Student

  • "We are comparing port removal rates in patients who were neutropenic vs normopenic at the time of port placement. We are looking for a statistician to help us analyze data and I was told this clinic was the place to start getting help with that."
  • 3500 patients with ports placed; patients excluded if there was no neutrophil measurement within two weeks of port placement
  • 184 neutropenic patients out of ~3500; hypothesis is that these patients have higher rates of port removal than normopenic patients
  • Ports could get removed for different reasons
  • Recommend talking to synthetic derivative team to see if they can quickly extract data on all these patients, to save Andy from having to build dataset himself; drawback = deidentified, so no going back and getting extra info later
  • First step: come up with a detailed list of fields you'd want from the EMR/SD - potential confounders, reason for port removal, list of infections, anything you can think of
  • Take that to SD team and see how feasible it would be to get that data
  • When it's time for data collection + analysis, recommend applying for VICTR biostatistics voucher; this should fit within the 90-hour project time frame
  • Recommend data collection in REDCap, pending discussions with IRB
 

2017 October 4

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Breanna Thomas, Meharry

  • Looking at relationship between subjects being of multiple minorities (LGB + racial minority) and anxiety, depression and substance abuse
  • Parent study is longitudinal, plan is to look at a cross section; need to make sure you know how the data (from UM) was subsetted and sent
  • Data is from parent study of 18-59-year-olds; detailed codebook and data info are available online
  • Anxiety, depression, and substance abuse are currently coded in data as yes/no variables with 45-55% prevalence (based on quick glance); make sure you know exactly how these are determined (self-report, questionnaire...?)
  • Potential confounders: age (leave as continuous if possible! - looks like it is categorized in data; ask if raw data is available); employment status; possibly others
  • Come up with complete list of confounders and prioritize them in order of importance
 

Justin Shinn, Otolaryngology

  • "Data is now collected regarding neck cancer metastasis in those with smaller tongue cancers. Want to compare retrospective group in those who recurred in the neck to those who did not based on pathology results."
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  • 15 years of data with about 75 patients with T1/T2 tongue cancers with some followup of neck observation
  • Have some followup out to 60 months; main time period of interest is two years
  • Main outcome of interest is recurrence (particularly in neck); 10 patients died within 5 years, but most recurred prior to death (two died without recurrence)
  • Idea is that doing more neck dissections in certain patients could help prevent recurrence; no one in database had a neck dissection
  • Recommend time to event analyses with outcome = time to recurrence; patients who never have recurrence will be censored at end of followup period (two years?) or last contact
  • With above approach, patients who died without recurrence would be considered under competing risks, but with small sample size and very few of those patients, probably not worth worrying about
  • Multivariable approach = Cox proportional hazards model
  • 36 recurred out of 74 patients
  • Main exposure = tumor depth, measured in mm
  • Possible confounders: margin (positive - did they get it all? - and/or close); T1 vs T2 (use tumor size instead of categorization? - but only 7 patients in this cohort had T2)
  • Write analysis plan a priori and stick to that (can include secondary analyses, but don't push your data too hard - with 36 events, it will be hard to tell much in detail)
  • More data would be available looking at patients with neck dissections, but would be hard/long to get; getting that would allow you to say "what are the chances of recurrence if I do vs don't have the dissection, assuming all other factors [tumor size, etc] are the same?"
  • Software: SPSS is most user-friendly - if you use it, make sure you turn on a log so you can reproduce results; if you go with Stata, UCLA has good examples/docs (https://stats.idre.ucla.edu/stata/ )
 

2017 September 27

Jeffrey Weiner, Pediatric Cardiology

  • "I am evaluating a database with clinical risk factors for post-operative thrombosis in congenital heart surgery. I am evaluating known risk factors (age, weight, severity of disease/surgery, cardiopulmonary bypass time) with genetic data (genotypes for known prothrombotic SNPís) to see if I can create a novel risk prediction model. I am having trouble (mostly software related as I am new at this), and would love a biostatisticianís insight."
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2017 October 11

Konrad Sarosiek, Plastic Surgery

  • "We have a large data set of ~60,000 patients who underwent different surgical procedure combinations under the guise of Ďmommy makeoverí and we are looking to see if there is added risk when combining procedures. We are looking to find relative risk & to isolate frequent complications & identify risk factors."

2017 October 4

Justin Shinn, Otolaryngology

  • "Data is now collected regarding neck cancer metastasis in those with smaller tongue cancers. Want to compare retrospective group in those who recurred in the neck to those who did not based on pathology results."

2017 September 27

Jeffrey Weiner, Pediatric Cardiology

  • "I am evaluating a database with clinical risk factors for post-operative thrombosis in congenital heart surgery. I am evaluating known risk factors (age, weight, severity of disease/surgery, cardiopulmonary bypass time) with genetic data (genotypes for known prothrombotic SNPís) to see if I can create a novel risk prediction model. I am having trouble (mostly software related as I am new at this), and would love a biostatisticianís insight."
  • Data on ~1000 patients, with 11% prevalence of thrombosis
  • Among other predictors, genotype is of particular interest - 7 SNPs, each with three subtypes; different SNPs have variable prevalence rates
  • Current approach: lump all SNPs into a single yes/no variable; covariates are age, weight, surgery, genotype
  • Planning to get VICTR voucher; data is in REDCap
  • Suggest looking at relationships between the genotypes: if someone with gene X always has gene Y, including both in model can be problematic; also, if very few patients have
  • Complexity of the model will be limited by minimum of (events, non-events) - in this case, roughly ~110 patients have thrombosis, so can fit 10-11 degrees of freedom max ("df" is kind of like a variable, but not exactly)
  • Clotting rarely happens in the first few days after surgery; for this reason, could consider time to event model (Cox) where event is time to clot, but would ideally account for a) time-varying covariates (severity of illness, etc) and b) competing risks (if patient dies before having a clot)
  • Abstract deadline in two weeks; for that, recommend current logistic regression model among all patients and only among hospital survivors. Hopefully those results are similar, but if not, emphasize results among survivors, because mortality could be a big source of bias and confounding in this study.
  • We believe this project would fit into the 90-hour VICTR voucher category.

2017 September 20

Pooja Santapuram, Hearing & Speech Sciences

  • "The purpose of this study is to examine the relation between language development and eye gaze patterns to audiovisual speech specifically in infants at risk for autism spectrum disorders (ASD). ASD is a developmental disorder characterized by social and communication deficits in addition to repetitive and restricted behaviors. It is known that infants at 1 year of age who later go on to be diagnosed with autism look at individualís faces less frequently (Osterling et al., 2002) and that toddlers (18-24 months) later diagnosed with ASD use fewer vocalizations with speech sounds and greater ďatypical vocalizationsĒ when compared to typically developing (TD) toddlers (Plumb & Wetherby, 2013). Yet, ASD is typically not reliably diagnosed until 2-3 years of age. Therefore, characterization of eye gaze patterns to audiovisual speech and vocalizations in high-risk infants may facilitate earlier identification of ASD and may also allow for future studies on potential treatments in this clinical population. Questions Iíd like to address or basically how best to approach an analytic plan for this study."
  • For first project, recommend calculating sample size for correlation statistic using precision (confidence interval width). Use Spearman correlation (nonparametric; does not assume that variables are normally distributed).
  • Linear regression with skewed variables: 1) Can run model and check assumptions - not interested so much in individual variables as in whether overall model fits well and meets assumptions. Try RP plots, QQ plots. 2) If assumptions are not met, can try transforming individual variables to improve overall model fit; also recommend using spline terms (or polynomials, if restricted to SPSS). This allows associations to not be straight lines, which is usually more accurate.

Joshua Arenth, Pediatric Critical Care

  • "Follow up session regarding best approach to log data into redcap for analysis as discussed a previous clinic."
  • Previous clinic notes
  • Planning to reapply for expired VICTR voucher to analyze pilot data of provider communication intervention. Discussed a longitudinal REDCap database with one demographic form (filled out at session 1 only) and a questionnaire form, filled out at sessions 1, 2, and possibly 3 (for control group only).
  • Each questionnaire's score will be an integer, 0-11. Recommend a Wilcoxon test (nonparametric version of paired t-test).
  • Planning to pitch multicenter trial for this intervention; sample size will be determined once this data is analyzed.

2017 September 13

Brian Adkins, Pathology

  • "Allo-antibodies against red cell antigens in pregnant women lead to poor fetal outcomes. As such OB/GYNs follow serial antibody titers.Traditional tube titration in slow and subjective. Automated gel titration is available but testing requires further understanding before clinical implantation. We are trying to figure out sample size and number of tests we should be running to determine clinical cut offs for antibody levels."
  • Suggest weighted kappa to address agreement between level of titration that each sample method detected antibodies at.
  • Need to get data into a software-readable format; look at the "spreadsheet from heaven" example here.
  • Variables are all categorical (1/2/4/16/etc for levels, 0/1 for differences) so nonparametric tests will likely not help.
  • If VICTR voucher is requested, this will fit under the 90-hour limit.

Paula Smith, Surgery

  • "I have a data set I am trying to run some stats on using the Stata program and I have questions about the best tests to run and how to make my data set compatible with Stata."
  • General recommendations: Use a do file in Stata to save analysis approach; write analysis plan a priori to define cohort; keep continuous information as much as possible rather than categorizing (eg, if raw BMI data is available, use that rather than categorizing)
  • Planning to submit abstract in October for conference in April; may submit abstract based on analysis already done, and work with VICTR on multivariable regression/competing risks
  • Main research question: Do adrenaocortical carcinoma patients with more resection have better/worse survival and risk of recurrence after surgery?
  • Currently analysis does not adjust for confounders or account for competing risk of death in recurrence outcome
  • Currently: used descriptive stats, KM curves; can get logrank p-values for KM curves, but need to look at proportional hazards assumption (do the curves cross? - but look at this in context of how many patients are "left" when they do cross)
  • Possible future recommendations: multivariable Cox proportional hazards model adjusting for potential confounders (age, BMI, surgery type, etc); for recurrence model, may need to use competing risks
  • This would fit into a 90-hour VICTR voucher.

2017 September 6

Shriji Patel, Ophthalmology

  • "I am conducting an analysis of Medicare Part B Claims Data and would like assistance regarding which statistical methods would be helpful in identifying trends in claims data."
  • Medicare Part B only has five years of data, only summary data available. Might be able to look into time series, but not certain that those methods will be helpful. Recommend good descriptives/visuals.

Nick Dantzker, Orthopedics

  • "Study to establish which radiographic parameters correlate with functional outcome and patient satisfaction in operative distal radius fractures. Need assistance with model for intra/interobserver reliability of radiographic measurements and overall statistical model for project"
  • ~55 wrists (final total could be up to 165, but more likely to be ~60) with injury x-rays pre-op, post-op and long-term; have 7 radiographic parameters on each at three time points (VAS pain scores, radial inclination, etc), as well as three injury ratings (one per patient); some patients have both wrists included
  • Main questions: 1) how good is interrater agreement on measures, often in degrees, and 2) are these measures (at one or both time points) actually predictive of outcomes?
  • Because a few (~4) patients have both wrists included, recommend randomly selecting one wrist from each - otherwise, confounders and outcomes from those patients will be more correlated than outcomes from different patients
  • Interrater agreement: suggest Bland-Altman plots (kappas are for categorical measures) - you don't want to see a pattern (eg, differences in agreement based on true value) ( additional link)
  • Intrarater agreement: suggest repeating measurements on ~15 wrists
  • Could do multivariable regression: outcome = [confounders - age, injury type, etc] + any x-ray info that will always be present + [one measurement, eg radial inclination]; run separate model for each measurement of interest
  • Regression is limited due to sample size - if you fit too many things in your model, it will not be generalizable to any other study (won't replicate), and we anticipate about 60 patients
  • Type of regression model will depend on exact outcome you're looking at
  • Three time points - could include an interaction term, but would likely be underpowered. Could also look at each time point with a separate model.
  • How many models are we talking? 10 measurements; __ outcomes; three time points - lots of models
  • Cohort is limited to a select subset of patients who have all three followup time points, isolated injury, respond to followup question - eg, need to be careful about how you generalize this to a general ortho population (patients with complete followup will be different from patients who are lost to followup)
  • Look at demographics of patients who responded to survey vs those who didn't - these will likely be different, which could bias results
  • Suggest applying for VICTR voucher; this will be <90 hours (typical manuscript project)
  • Feel free to come back for input on REDCap database, further discussion

2017 August 30

Jennifer Watchmaker, Radiology

  • "I would like to perform I believe an ordinal logistic regression. I have outcome data and also a continuous variable. I would like to know if the continuous variable (obtained pre-procedure) predicts outcome. I would like to also gain a sense of what additional analysis I can do with my dataset. I have 300 procedures worth of data on redcap."
  • 300 procedures (multiple procedures per patient - about 175 unique patients) are being reviewed by radiologists, currently only one read each; recommend having at minimum a random sample of these reviewed by multiple readers to gauge interrater reliability
  • Main outcome is ordinal, ranging from no response (0) to full response (3)
  • Main exposure is NLR (neutrophil:lymphocyte ratio) - hypothesis is that patients with a higher NLR are less likely to have a treatment response
  • Collect info at time of procedure and two months post-procedure; so far have excluded patients who are lost to followup for any reason
  • Recommend a proportional odds logistic regression model due to ordinality of outcome; will need to adjust for the fact that there are multiple procedures per patient
  • Do *not* do univariate testing to determine what covariates to include in the model; rather, decide based on clinical knowledge/literature review what are important potential confounders. Rough estimate is that you could include 10-15 parameters in this model.
  • Examples on how to do ordinal logistic regression in R (https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/) and Stata (https://stats.idre.ucla.edu/stata/dae/ordered-logistic-regression/); look into clustered sandwich estimation of variance to account for within-patient correlation
  • If not enough time to figure out clustered sandwich estimation before abstract submission, recommend using just first procedure per patient and applying for VICTR voucher
  • Additional reason to get VICTR voucher: likely that NLR has a nonlinear relationship with the outcome, which makes an accurate model more complicated to fit and interpret
  • If VICTR voucher is required, this project will require 90 hours or less

2017 August 23

Rachel LaBianca, Critical Care Pharmacy

  • "Studying open fracture orthopedic trauma patients to determine whether there is a difference in infection rates for patients receiving antibiotics within 60 minutes of presentation versus those receiving antibiotics >60 minutes from presentation. Would also like to conduct analysis to identify other factors impacting infection rate. Would like assistance with statistical design and help in determining whether VICTR application will be needed for this project."
  • They anticipate approximately 100-200 subjects in their analysis with about 20% infection rate. Their primary outcome is infection rate; therefore, they are somewhat limited on the complexity of the model they can fit without using data reduction techniques.
  • They have identified potential confounders and will put them in order of importance.
  • We discussed the use of splines for the time to antibiotic if that is their primary exposure of interest. We also discussed whether they could fit a model with type of antibiotic, categorized into 3 main categories as their primary exposure of interest.
  • They will return to clinic to discuss setting up the data to ensure a smooth transition to the analysis phase.
  • The analysis is fairly straight-forward although could be a bit more involved if some kind of data reduction technique is used, such as propensity scores. The analysis should easily be completed within 90 hours giving enough additional time for manuscript preparation/revision.

2017 July 26

Joshua Chew, Pediatric Cardiology

*"We are completing a retrospective study evaluating a new echocardiographic measure (pulmonary pulse transit time; pPTT) in pediatric pulmonary arterial hypertension (PAH). Our cohort includes roughly 20 PAH patients with 2:1 age/sex matched controls. Our initial analysis demonstrated a difference in pPTT between PAH patients and controls. We also saw an association between pPTT and a crude measure of right ventricular function. We are now performing follow-up measurements to obtain an objective measure of RV function (myocardial performance index; MPI). We would also like to explore the relationship of pPTT with hemodynamic data and clinical outcomes in PAH patients over time. The questions we would like assistance with are as follows: 1. What is the most appropriate approach to evaluate the relationship between pPTT and MPI, taking into account that we suspect it may not be linear? 2. Would appreciate recommendations on analysis plan for the longitudinal analysis of pPTT in PAH patients. What sorts of hemodynamic data/outcome measures are most appropriate? How do we deal different follow-up times and different times between echocardiograms? How do we account for patients being on different therapies during the follow-up time?"
  • We discussed the use of splines to relax the linearity assumption in any models that may be fit. In order to avoid over fitting with linear regression, we follow the rule of thumb of estimating 1 parameter for every 10-20 subjects.
  • We discussed the use of spaghetti plots to illustrate the trajectories of the pPTT over time in the 20 PAH patients, including using colors to indicate those with different therapies or who may have died.
  • As a potential secondary analysis in the 20 PAH patients, a mixed effects model with a random intercept could be used with pPTT as the outcome and time and type of therapy as the covariate, including an interaction. The number of classes of therapies will need to be discussed given the small sample size and the potential for over-fitting.

Rachel Sosland, Urology

  • "Urinary tract infections may affect as many as one third of patients undergoing intradetrusor onabotulinumtoxinA (BTX-A) injection for medication-refractory overactive bladder (OAB). We have retrospectively collected data on 70 patients undergoing intravesical botox injection in 2016 and seek to identify potentially modifiable risk factors for post-operative UTI in patients with non-neurogenic OAB. Several of these patients have undergone multiple injections. We would like to assess their risk for UTI over time and over multiple different injections. We are seeking statistical support to assist in determining the best way to analyze this data in the same patient over time with multiple injections."
  • We discussed several options to address their question of interest. One potential option is to use Poisson or negative binomial regression with the number of UTIs as the outcome for a given person adjusting for covariates of interest such as where the injection was received (OR or clinic), class of antibiotic received (number of classes will need to be discussed to avoid over fitting), and including the varying follow-up times as an offset.
  • We also discussed how to address the question of risk factors for multiple UTIs. Subsetting on those who had at least 1 UTI, a logistic regression with the outcome indicating whether the subject had > 1 UTI and adjusting for pre-determined covariates would potentially address this.

2017 July 19

Cyrus Adams, Surgery/Urology

  • "We are currently investigating patient factors (demographic and clinical) in a group of adult patients with congenital genitourinary disorders. We currently have a redcap database of ~150 patients who recently presented to the adult clinic meeting this criteria. We are interested analyzing patient demographic and clinical factors that may be associated with renal dysfunction at the time of presentation to the adult clinic (measured by decreased GFR and/or the presence of hydronephrosis or renal scarring)."
  • Second question: Renal dysfunction measured by GFR - typical cutoff is <60, but we recommend also analyzing with GFR as a continuous variable (allows you to keep all information and not make false dichotomies). Only 9 patients had renal dysfunction when categorized, so definitely recommend keeping that outcome continuous. This outcome is fairly normally distributed, which is helpful.
  • First question: predictors of being followed as pediatric patients. Outcome is determined by MDs via chart review. Have 58 Nos ("non-events") and 93 Yesses ("events"); can reliably fit about six degrees of freedom (roughly equal to six variables) in a logistic regression model.
  • Recommend doing graphs of descriptive statistics, overall and by pediatric followup status.
  • Prioritize potential covariates in order of importance/relevance; consider missingness when doing this (if a variable is clinically important but only measured in the hospital, eg education and health literacy, it is less helpful here).
  • Plan is to apply for VICTR voucher.
 

2017 May 17

Sara Nelson, Anesthesiology

  • "I visited about a month ago to talk about our drug cost project. I've fit a model, but have some concerns that the assumptions haven't been met. I would like to meet and talk about the current model and possible alternative options such as random/mixed regression. If possible, it would be great to pull up the analysis in R (I can bring my laptop)."
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 I am looking for guidance on how to proceed with performing a validation of the bedside swallow screening used for acute stroke patients in the ED, neuro ICU, and the neuro care unit.
The project is development of risk prediction models for placement of a ventricular assist device vs medical management with outcomes of survival to transplant and 1 year post transplant survival in pediatric patients.  Considering using propensity matching due to variability within groups.
I am planning a clinical study and would like my sample size calculations to be reviewed by a biostatistician before I submit for VICTR funding.
The study is a randomized, double-blind, experiment in human volunteers examining the effects of a drug commonly given to liver failure patients on oral glucose tolerance.
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Pooja Santapuram

 

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2017 March 8

Kristy Broman, Surgery

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2017 May 17

Sara Nelson, Anesthesiology

  • "I visited about a month ago to talk about our drug cost project. I've fit a model, but have some concerns that the assumptions haven't been met. I would like to meet and talk about the current model and possible alternative options such as random/mixed regression. If possible, it would be great to pull up the analysis in R (I can bring my laptop)."
  • Collinearity: try Hmisc::redun() for a redundancy analysis, or varclus() from same package.
  • Might try a negative binomial model due to distribution of cost outcome. To incorporate random effects, might try lme4::glmer.nb()
  • Available variables: procedure code; attending anesthesiologist (248 unique); in-room provider (CNA/resident; 572 unique); surgeon; surgery start time; duration; ASA class; age; gender; case type (surgical specialty; 9 unique); institution (VUMC/MGH); cost; base relative value units (?); provider team (3500 unique)
  • No data available on specific medications used
  • Is duration a strong surrogate for complexity? Strong enough to leave out case type?
  • Possibility: cost ~ ASA class + age + gender + duration + institution + age*institution + ASA*institution, random effect = attending; adjusting for other variables (case type, etc) is likely an artifact of things like duration and will result in collinearity
  • Adjusting for attending would get at what is likely driving at least part of the cost, which is sedation choice, but no way to tease that out

2017 May 17

Tanya Marvi, SOM, Medical student

  • "My project is looking at platelet count in patients with musculoskeletal infection. Patients were categorized into local, disseminated, and complicated infection, and I used an ordinal logistic regression to see if we could predict how they would be categorized based on their day 1 platelet count. Additionally, I used rloess to look at the trend of the platelet counts among the different groups. I want to make sure I am interpreting the results correctly and see what other analysis I should consider."
  • The outcome of interest is a 3-level outcome describing severity of infection in patients who present to the ED and are subsequently admitted. One question of interest is whether there is an association of platelet count and type of infection diagnosed. A proportional odds model was fit with platelet count as the single covariate. We recommended fitting splines to the platelet count (3 knots should be fine, given the sample size), and clinically determining what potential confounders are of importance to include in the model. Proportional odds assumptions should also be checked.
  • Because subjects had differing lengths of stay in the hospital, a Cox model could also be fit with time to discharge as the outcome and adjusting for pre-selected confounders.
  • A second question of interest is whether time-varying platelet count is associated with type of infection. A proportional odds model can be fit with robust standard errors. There should be an option in Stata to request the robust estimates.

2017 May 10

Sara Nelson, Anesthesiology

  •  =We performed a study assessing the variation in anesthetic drug costs. We did so by creating multivariate linear regression models in R. We've received reviewer comments with various suggestions we would like to implement, such as combining two of the models. I believe this will involve creating a nested variable; however, I am struggling with getting this to work in R.=
  • Current models have a fixed effect for in-room provider (561 df). Suggest replacing this with a random effect, or possibly a nested random effect (attending -> in-room provider). R package nlme or lme4 (newer) might be good for this.
  • Might also look at interaction between case type and duration.
  • Make sure that costs are the same per patient regardless of insurance.
  • Make sure to check model assumptions, even after outcome is transformed.
  • For all model covariates, show a point estimate + CI for the effect on cost. For age especially (modeled with restricted cubic splines), would be good to also show a visual of age vs cost.
  • Potentially reframe paper as "potential predictors of cost" vs "how much variance in cost can we explain."

Bryan Hill, OB/GYN surgical fellow - CANCELLED

  •  =Reporting complications after surgery are important for quality improvement. Two methods of finding complications are: 1) administrative data from diagnosis codes and 2) key-word search from a manual chart review. We suspect the administrative reporting method, under-reports complications. Primary aim: determine sensitivity, specificity of the administrative method compared to the manual reporting method Secondary aim: Determine which risk factors are associated with having a complication. #1: Question for statisticians: would the best way to look at our secondary aim be to create a regression model with the outcome "complication" and variables age, body-mass index, setting (outpatient or inpatient), sling type, attending, anesthesia time, operation time, smoking history, diabetes? #2 Sling type is heavily dependent on attending (they like to chose a particular brand or type). How do we adjust our model for that?= 

2017 May 3

David Leverenz, Internal Medicine

  • "We have developed an educational podcast for our internal medicine residency program. We are studying the effects of this project through pre and post-intervention surveys. I would like assistance in the statistical comparison of pre and post-intervention survey results."
  • Emphasize descriptive statistics (pre vs post) over p-values. If p-values are needed, chi square tests for categorical variables and Wilcoxon/Mann-Whitney tests for percentages are useful. Also think about boxplots to show variability in data instead of a single summary statistic.
  • Include measures of variability (interquartile ranges) as well as summary statistics (median).
  • Make sure and address differences in response rate in pre vs post, and describe any differences in patient populations.

2017 April 26

Susan Smith, Critical Care Pharmacy

  • "Purpose of project is to examine the efficacy of a short versus long duration of antibiotics for the treatment of intraabdominal infections. We would like help with our binary logistic regression model."
  • This is a really complex analysis due to immortal time bias, confounding, etc. Option A would be a Cox model for time to treatment failure with primary exposure = daily antibiotic use. This is a complex model to fit for a non-statistician; suggest contacting VICTR to see about stats support for this.
  • Option B... maybe a Kaplan-Meier curve removing patients from N at risk as they go off antibiotics?

Jamie Robinson, Surgery

  • "I would like assistance with a regression analysis looking at factors that may affect survival after portoenterostomy for biliary atresia."
  • Data set has 48 patients with this rare condition; about 20 had the outcome of interest (transplant or death). This limits what can reasonably and reliably be put in a survival model.
  • Consider lag time between being placed on the transplant list and actual transplantation - would be good to describe this.
  • When fitting model, use a Cox proportional hazards model (coxph in R's survival package; outcome will be created with the Surv() function; look at vignettes and/or look for UCLA tutorials). Descriptives and qualitative info will be helpful with a small population.
  • Choose a common followup time - maybe five years, two years? Look at minimum/maximum followup time to determine.

2017 April 12

Nishant Ganesh Kumar, Plastic Surgery/Medical School

  • "Would like to conduct a multi-regression analysis of opiate use and hospital length of stay against other variables being studied in an Enhanced Recovery after Surgery protocol for microsurgical reconstruction."
  • Primary outcomes are hospital length of stay and total opioid use. Hospital LOS has a very skewed distribution; original analysis used linear regression. We recommend checking RP plots and, if assumptions are not met, using either ordinal logistic regression or a Cox proportional hazards model with time to hospital discharge as the outcome.

2017 April 5

Ashley McCallister, Pharmacy

  • "My research project is in the NICU on Vitamin A use. I need help identifying what types of statistical tests should be run on the data."
  • Primary outcome is BPD (yes/no); secondary outcomes are discharge on oxygenation and days on the ventilator
  • Currently patients who died in the NICU are excluded; this will present severe limitations due to confounding, but without statistical support it is complicated to account for death when including all patients
  • For dichotomous variables, can use chi square test (vitamin A exposure vs BPD, eg). For days on the ventilator, use a Wilcoxon rank sum test (like a t-test, but does not assume normality)

Ida Aka, Clinical Pharmacology

  • "I need help with my sample size calculation for my PPI and SSRI projects. Both projects are looking at CYP2C19 *2 and *17 variants."
  • Extended discussion on how sample size/power can vary depending on genotype proportions in the sample; will need to investigate distributions of both genotypes and outcomes to decide how many patients are feasible to genotype and what kind of tests to use in eventual analysis

2017 March 23

Kristy Broman, Surgery - No Show

 
  • "The question I am trying to answer is whether there is a way to compare two incidence ratio. I am using the SEER database and SEER Stat which has built in modules for calculating age standardized incidence ratio for specific events. The output I get is the total N, the total event number, and the standardized incidence ratio. This is essentially the ratio of observed to expected, but I cannot know how the expected is determined (this is a "black box" within the module. So I want to know if there is a way to essentially compare the already calculated standardized incidence ratios."
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2017 March 15

Leslie Fowler, Anesthesiology

  • "Prior to developing a Residents as Teachers curriculum within our department, Dr. Robertson and I sought to gain insight into the teaching perspectives of our residents by administering the Teaching Perspective Inventory (TPI). We administered a follow-up survey to gather information regarding dominant and recessive teaching perspectives."
  • "Our manuscript was accepted for publication with revisions. One reviewer notes indicated we should consult a statistician to see if raw data can be used for other statistical analysis as well as a T test. Another reviewer comments stated to consider researching a theoretical framework to base the research design. Should we conduct a T Test with data we collected? Is that the most appropriate? Can the raw data be used for other analysis?"
  • An already validated survey was used to evaluate teaching mode preference in 2nd, 3rd, and 4th year residents. This validated survey converts the raw scores to weighted scores in each of five different teaching modality preferences. Frequencies of primary teaching modalities were computed according to whether the resident planned an academic or private practice career. In order to assess whether there was a difference in the distributions of the frequencies of primary teaching modalities across the academic/private practice groups, we recommended a chi-square test. In addition, we recommended doing a Wilcoxon Rank Sum test using the raw scores across the two groups. Finally, in order to better visualize the distribution of the raw scores, we recommended creating box plots of the raw scores for each group.

2017 March 8

 

Amol Utrankar, Anesthesiology

  • "I am a medical student working with several members of Department of Anesthesiology on a project examining factors associated with in-encounter mortality among patients who are escalated to the intensive care unit following multiple rapid response team activation events, using a sample of 80 patients from 2016 VU rapid response data. I have several continuous and categorical variables of interest (Sepsis Related Organ Failure Assessment, organ dysfunction by system, age, gender, referring rapid response team, and hours elapsed between rapid response events. I would like to double-check my statistical methods with someone who has more experience in statistical analysis and Stata; I've been using chi-squared tests, Fisher's exact tests, and logistic regressions to assess associations, but want to make sure that I'm applying these tests correctly and setting up my variables properly."
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  • There are about 87 subjects in their data with about 20 deaths. They are interested in exploring the association of different risk factors with mortality. We discussed the rule of thumb governing how complex of a logistic regression model could be fit (include roughly one covariate for every 10-20 deaths).
  • They have more covariates of interest to include in the model than degrees of freedom allowed without over fitting. Therefore, we discussed avoiding using univariate analyses to drive model selection. Rather, clinical knowledge and literature reviews should help govern what selecting the models of interest.
  • There are several complications that are of potential interest as covariates. One way of including all of them in the model is simply to create an indicator for whether any complication occurred or not or to sum them up and include the total number of complications.
  • We also discussed ways of displaying the data to help tell the story. One suggestion was to create boxplots and strip charts of the number of hours between the first call to the ICU team to when the patient was elevated to the ICU floor, stratifying by survival status. Points on the graph could be coded by shape and/or color to indicate sex or age or any other categorical variable of interest.
  • A potentially more complicated analysis that would account for variability in the different ICU teams' threshold for elevating a patient to ICU would be to fit the logistic regression model with a random effect. This may not converge due to the small number of ICU teams (~4-5).
 

2017 March 1

Susan Smith, Critical Care Pharmacy Resident

  • "This is a retrospective study examining the effects of neuomuscular blockers on time to abdominal closure in trauma patients undergoing damage control laparotomy managed with an open abdomen. I would like help determining what type of regression is most appropriate to answer two different questions regarding my data set:
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2017 March 8

Kristy Broman, Surgery

  • "The question I am trying to answer is whether there is a way to compare two incidence ratio. I am using the SEER database and SEER Stat which has built in modules for calculating age standardized incidence ratio for specific events. The output I get is the total N, the total event number, and the standardized incidence ratio. This is essentially the ratio of observed to expected, but I cannot know how the expected is determined (this is a "black box" within the module. So I want to know if there is a way to essentially compare the already calculated standardized incidence ratios."

Amol Utrankar, Anesthesiology

  • "I am a medical student working with several members of Department of Anesthesiology on a project examining factors associated with in-encounter mortality among patients who are escalated to the intensive care unit following multiple rapid response team activation events, using a sample of 80 patients from 2016 VU rapid response data. I have several continuous and categorical variables of interest (Sepsis Related Organ Failure Assessment, organ dysfunction by system, age, gender, referring rapid response team, and hours elapsed between rapid response events. I would like to double-check my statistical methods with someone who has more experience in statistical analysis and Stata; I've been using chi-squared tests, Fisher's exact tests, and logistic regressions to assess associations, but want to make sure that I'm applying these tests correctly and setting up my variables properly."

2017 March 1

Susan Smith, Critical Care Pharmacy Resident

  • "This is a retrospective study examining the effects of neuomuscular blockers on time to abdominal closure in trauma patients undergoing damage control laparotomy managed with an open abdomen. I would like help determining what type of regression is most appropriate to answer two different questions regarding my data set: 1. Does neuromuscular blockade affect the time to abdominal closure following damage control laparotomy? 2. Does neuromuscular blockade affect the time to goal RASS? For the first question, at least one of the covariates is time-dependent. I also have a few specific questions regarding how to interpret the results form these analyses."
  • Recommend a Cox proportional hazards model for all outcomes (time to...). No need for time dependent covariates (all covariates are baseline).
  • Also recommend including patients who died before primary outcome (abdomen closure) - this will make results more generalizable to all patients who receive this procedure, vs. those who survive (which we can't know when a patient is admitted).
  • For help with SPSS, look for UCLA tutorials on Cox regression. Interpreting output

Brian Adkins, Pathology

  • "I am comparing rates of atopy in patients with allergic transfusion reactions. I need help calculating significance."
  • Strongly recommend collecting data for a control group (patients who received a transfusion and did not have an allergic reaction). Might be possible to do this using BioVU. With current data, you can describe the prevalence of allergies among patients who had a transfusion reaction, but can't draw any statistical conclusions about a difference in allergy rates between them and other transfusion patients.

2017 February 22

Sara Nelson, Anesthesiology

  • "We are looking to determine the effect of the pain consult service on mortality and morbidity in rib fracture patients. The protocol for the consult service was implemented in 2013. Our data is from 2010-2015, so I think there needs to be a before and after analysis utilizing matching. Mortality is the primary analysis, there are numerous secondary analyses--pneumonia, respiratory failure, 30-day vent free days, 30-day ICU free days, length of stay and tracheotomy."
  • 1152 patients seen by consult service after implementation; total data set has ~5000 patients, but not all data is available before protocol implementation
  • Raw mortality rates are 7% prior to implementation and 3% after; about 400 deaths in the data set
  • Recommend excluding patients seen after chest service began, but before official protocol implemented - too many unknowns and variables in this group to allow for clean conclusions
  • Main research question: are outcomes different among patients who met screening criteria, or would have met screening criteria, before and after implementation of the screening protocol? -> Need to exclude patients who never would have met screening criteria
  • Recommend Cox model for mortality, proportional odds logistic regression for other continuous outcomes, logistic for pneumonia, etc; limiting sample size is number of events (Cox model) or minimum of events/non-events (logistic)
  • Matching is probably the cleanest way to do this - match on age, number of rib fractures, ISS? Or match on propensity score: create model for propensity of being screened (among patients in post-implementation period) using data available, then use that model to calculate propensity score for all patients in pre- and post-implementation periods and match on that

2017 February 15

Elena Nedelcu, Pathology

  • I need assistance with choosing the right test to interpret correlation between variables and outcome and perform them
  • Data was collected on 324 liver transplant patients over three phases: baseline, practice changes, and post-implementation; main exposure is blood utilization, outcomes include LOS, mortality, discharge disposition
  • Potential for mixed effects model - want to account for surgeon
  • With multiple outcomes and end goal of manuscript, statisticians recommend 90-hour VICTR voucher (https://starbrite.vanderbilt.edu/funding/src/?action=create )

2017 January 25

Laurie Tucker, Department of Pediatrics - Postponed

  • Follow-up to previous clinic visit.
  • Data looks great. There are a few additional pieces on the list that Laurie is trying to obtain from StarPanel. Notes from previous sessions are below.
  • Clinic statisticians estimate 90 hours for VICTR application.

Johnny Wei, Medical Student/Anesthesiology

  • "I am a 3rd-year medical student who is working with the Department of Anesthesiology on a project investigating demographic and clinical factors associated with post-operative opioid use. In short, we are looking at what factors (i.e. age, sex, type of surgery, etc) are associated with having an opioid or benzodiazepine prescription at various time points in the 12 months after a procedure. I have a rough idea of what types of figures I would like to create, and have already created the initial iterations of them. However, because I am a relative novice regarding biostatistics and using my analysis software (Stata), Iíd like to discuss my methodology and my analysis process and see if Iím doing anything inappropriate with my data management or analysis. Most of the tests I have been running are chi-square/Fisherís and logistic regressions, and I would appreciate advice on the appropriateness or the optimization of these tests. In short, Iím more interested in someone looking over the work and code I have done so far, and seeing if there are any major red flags in my methodology rather than coming up with the analysis plan itself (although advice on the latter would be very much appreciated)."
  • Recommend removing p-values from Table 1, un-collapsing outcome to regain information lost in categorizing, and using Kruskal-Wallis test instead of one-way ANOVA.
  • One resource for sample size determination: http://biostat.mc.vanderbilt.edu/wiki/pub/Main/JenniferThompson/gcrc_samplesize_06192009_handouts.pdf

2017 January 18

Niels Johnsen, Urologic Surgery

  • "We are working on a project that attempts to determine predictors of bladder rupture in patients following blunt-trauma pelvic fractures. A prior study was performed at an outside institution with similar (or intended to be similar) methods using a smaller cohort of patients. We chose all bladder rupture patients plus control pelvic fracture patients without rupture (4:1) and have the data on these patients. The hope is to identify clinically significant predictors of bladder rupture based on fracture configurations and then to devise a clinical prediction model to risk-stratify patients who present with pelvic fracture for having bladder ruptures. I have attached the previously published similar study that I'm referring to as a reference and will bring the deidentified database with me on Wednesday."
  • Motivating paper uses univariate variable selection and stepwise backwards selection to create the final model. We do not recommend either of these.
  • We do recommend choosing a pool of potential predictors based on clinical knowledge and available data, prioritizing based on potential clinical importance.
  • 140 bladder rupture cases (minimum event size)
  • Reference - Frank Harrell's Regression Modeling Strategies (chapters on predictive modeling and data reduction)
  • Planning to submit VICTR voucher when mechanisms are available again (check with Lesa Black); we estimate 90 hours to develop and validate the prediction model and prepare manuscript

Joel Musee, Department of Anesthesiology

  • "We have put together a study to examine whether a commonly used perioperative device (Lifebox), can be used to alert clinicians of hypoperfusion. The lifebox is pulse oximeter and measures oxygen saturation on extremities. The monitor has a graphical read out made up of 15 bars with more bars associated with a better signal, a proven surrogate for perfusion. We hypothesized that mean arterial pressures of 55 or less would not lead to a perfusion signal of 5/15 bars. The biggest questions is how to best analyze the data to test our hypothesis and also what kind of power we would need for a study like this."
  • Recommend not dichotomizing unless absolutely necessary - above scenario, for example, treats Lifebox measurement of 6 and 15 as exactly the same, which is likely not true
  • Data will be manually collected by staff looking at blood pressure and Lifebox at the same time
  • Likely repeated measurements on each patient (during preop, while administering anesthesia...)
  • Recommend collecting information on other variables - age, amount of sedation at a given measurement, etc; make sure some kind of patient identifier is present
  • Use longitudinal database in REDCap for data collection
  • Recommend fitting a spline (search "restricted cubic splines") - device might be more closely associated with MAP at certain points than others
  • Could be completely separate patient populations - one could be women undergoing C-sections; might be advisable in this case to do subgroup analyses?
  • Will probably apply for VICTR voucher - estimate 90 hours
  • Think about how many patients (in each group?) are feasible to enroll and how many time points could be measured
  • Next step would be to repeat the study in Kenya - possible that we'd see different results if MAP tends to be different, for example

2017 January 11

Chelsea Isom, General Surgery

  • Updates to project.
  • Chelsea has cleaned the data and done some preliminary analysis - project will now probably take ~60 hours to complete. See notes below for details.

Maie El-Sourady, Internal Medicine

  • "I am an attending physician on the Palliative Care consult service and I have been collecting data from the learners that rotate on our service for the past 4 years. They compete a pre-test and a post-test evaluating their comfort level with basic palliative care topics. I would like help doing the statistical analysis with this data. I have several cohorts (medical students, internal medicine residents, visiting fellows, etc) but the test is the same for all of them."
  • Recommended starting with boxplots of raw data to look at differences in overall distribution for pre and post scores on each individual question. Excel doesn't have a template for this - look into SPSS.
  • Possible further investigation could involve collapsing into categories using Cronbach's alpha or similar and looking at differences in pre/post by type of learner (resident/student/other).
  • Recommend asking if there is access to a biostats collaboration plan; if not, and if further analysis is needed, can apply for a VICTR voucher (probably 40 hours).

2017 January 4

Laurie Tucker, Department of Pediatrics - Postponed

  • Follow-up to previous clinic visit.

2016 December 28 - canceled due to holiday

2016 December 21

Chelsea Isom, General Surgery -- Postponed

  • Updates to project.
 

2016 December 14

Laurie Tucker, Department of Pediatrics -- Canceled

  • Follow-up to previous clinic visit.
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2016 October 12

Debra Braun-Courville, Pediatrics

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2016 December 14

Laurie Tucker, Department of Pediatrics -- Canceled

  • Follow-up to previous clinic visit.

Andrew Smith, Pediatrics/Anesthesiology - No show

  • I am currently embarking on a multicenter look at variations in value delivery to critically ill children across congenital cardiac surgical centers across the US, using data merged from two data streams, a clinical registry (Pediatric Cardiac Critical Care Consortium or PC4) and an administrative data set (Pediatric Health Information Service) to try and pull together the numerator and denominator of the value equationÖ I was wondering who would be able to help me think about how best to look at cost (and value) comparisons from a statistical standpoint, with respect to outcomes including mortality. Specifically, given that some children die relatively soon after surgery, they may not incur substantial cost though one would also argue that they didnít get the ď alue" they wanted from their episode of careÖ Iím thinking about this from a censoring and survival curve/Kaplan-Meier standpoint, but Iím sure it is more complex than thatÖ which is where I think some healthcare economic statistical prowess would come in handy.

Kazeem Oshikoya, Clinical Pharmacology

  • Requesting help with interpretation of a data analysis.
  • Looking at risk factors for composite adverse event (change in BMI, increase in blood sugar, others) among pediatric patients prescribed risperidone for at least four weeks. Observation period is 16 weeks (>=4 weeks of risperidone + additional weeks up to 16). Eventually will look at genetic variants but focusing on this for now.
  • Currently has data on 210 patients; among these, has 45 events. Number of parameters that can be included in a logistic regression model is the minimum of (events, non-events) / 10-20. So, with 45 events, can include 4 (maybe 5) parameters; any more, and the model will be overfit, meaning it will be perfectly fit to this data set but will have radically different results if applied to a different cohort.
  • Recommend not doing testing on univariate descriptives: for example, might be OK to describe age among patients prescribed risperidone on vs off-label, but don't test this difference. Can be misleading due to presence of confounders. Use clinical judgment/literature to prioritize which covariates should be included in the model.

2016 November 30

Laurie Tucker, Department of Pediatrics

  • Follow-up to previous clinic visit.
  • Went over spreadsheets of data collected since last visit and made suggestions: data dictionary to indicate what each variable level means; get ICD9 codes in addition to CPT codes, and think about how to cluster ICD9 codes; think about clinical outcome variables to represent general questions of interest (eg, we can model language category vs level of ED triage).
  • Plans to straighten out data issues and come back to clinic 12/14.

Alexander Hawkins, Department of Surgery - had to cancel

  • "Working with patient satisfaction scores and looking at association between disease processes. Would like help with how to interpret scores and adjust for providers, pain scores, etc."

2016 November 23 - canceled due to holiday

2016 November 9

Chelsea Isom

  • " Approximately 20% of patients with colorectal cancer (CRC) present with metastatic disease-most commonly to the liver or lungs. Successful resection of these metastatic foci leads to significant long-term survival. Less commonly, patients present with isolated metastasis to non-regional lymph nodes (NRLN) and little is known regarding the role of resection in these patients. The primary aim of this study is to evaluate the outcomes of patients with CRC who undergo resection of NRLN metastasis. A retrospective cohort study of patients diagnosed with CRC and NRLN metastasis was performed using the Surveillance, Epidemiology, and End Results database (2004-2012). Demographic and clinical factors will be compared for patients who underwent resection of NRLN metastasis and those who had not. Kaplan-Meier and log-rank analysis will be used for survival analysis. Logistic regression analysis will be used to assess factors associated with resection of NRLN metastasis."
  • Data set is one record per patient, 829 patients of interest. Limited data available on potential predictors (registry data). Suggested things to look into: propensity for getting surgery vs not, competing risks, multiple mortality models looking at patients who have had opportunity for at least 1 year of followup and the subset who has had at least 5 years of followup.
  • Estimate about 90 hours for data management, analysis, manuscript writing and revisions.

Mark Clay & Ashley Newell, Pediatrics (Cardiology/Critical Care)

  • "Restrospective project looking at increased BMI as a risk factor for increased resource utilization in patient after Bidirectional Glenn procedure. The data was previously analyzed use a Loess Regression using R software. The data has been edited and we are seeking help with repeat analysis and graph generation."
  • Need to look at model assumptions for prior analyses - for example, residual vs predicted plot. Based on distribution of LOS and ventilator hours, we have concern that model assumptions are violated and therefore the model results would not be reliable.
  • If that's the case, look into perhaps a negative binomial model in R (function glm.nb() in the MASS library.
  • Continue keeping Z score for weight as a continuous variable in the model. Consider adding patient location (followup at VCH vs clinics in other areas) to model, but may need to prioritize covariates: With 109 patients in a linear regression model, can only have ten degrees of freedom (roughly corresponds to covariates) and still trust model results.

Leah Hauser, Otolaryngology

  • "Studying olfactory (smell) dysfunction in CRS. There is some prior evidence that tissue eosinophilia contributes, but this role is controversial. Our 3 major questions are: 1. Does objective olfactory function measured by age/sex adjusted UPSIT score correlate with tissue eosinophil counts?; 2. Is olfactory function in eosinophilic CRS due to tissue eosinophilia or disease severity?; 3.Is the effect of eosinophilia on olfactory function associated with type of CRS (CRS vs CRSwNP)? We think that preliminary data analysis shows that eosinophils counts (column J) correlate moderately with UPSIT score in CRSwNP but not at all in CRS(without NP), but we suspect this may be due to worse disease rather than the eosinophils themselves. We are not sure how to best analyze our data to determine the etiology of olfactory dysfunction."
  • Looked at distribution of outcome (UPSIT scores, raw and adjusted); distribution is bimodal, which makes linear regression problematic. Consider other regression options like proportional odds (aka ordinal) logistic regression for multivariable associations.
  • For univariate associations, Pearson correlations are probably invalid for the same reason; use Spearman (rank) correlations instead.
  • Clinically investigate reason for bimodal distribution.

2016 October 26

Katie DesPrez, Critical Care?

  • "Retrospective clinical project on ARDS. Briefly, I am interested in understanding whether my correlation between the variable I've called OSI and mortality is valid even in patients who have no blood gas (i.e., in this data set, patients who do not have the variable OI). Preliminarily it does not seem to be, but I'm wondering whether this is because the data is underpowered for that particular analysis."
  • Using Stata to compare non-nested ROC curves: http://www.ats.ucla.edu/stat/stata/faq/roc.htm
  • Could also do a model with both oxygenation variables and see whether blood gas version adds additional predictive value after adjusting for pulseox version.
  • Also suggested looking at time to death (vs died/survived), and looking at SSDI for death dates especially for patients discharged to hospice.

Laurie Tucker, Department of Pediatrics

  • "A project looking at the acute health care utilization patterns of non-English speaking patients in comparison to English speaking patients. The study is set up as a retrospective cohort study. We have gather data from Star Panel, and I would like a bit of help determining the next steps in analyzing the data."
  • First step is to determine who exactly data has already been collected on: patients who were already established as of July 2013, or does it include patients who were born or were established after that date? If the former, everyone should have the same followup time; if the latter, need to deal with different followup times in analysis.
  • Also think of potential confounders for relationship between language group and rate of acute care visits - does one group have higher severity of illness, for example.
  • Recommend coming back to clinic after discussion with data colleagues. Planning to apply for VICTR voucher.

2016 October 19

Billy Cameron, Surgical ICU/Trauma

  • "I am currently working on a project to justify a Nurse Practitioner team in the Trauma division at VUMC. We performed a 12-week pilot, for which we have good data showing decreased length of stay. One of the data points was to compare an acuity scale: Injury Severity Score (ISS) to show that acuity remained pretty even from the previous year before the pilot compared to the pilot period. I am trying to figure out what the statistical significance of the difference is (we are wanting to show that the level of patient acuity according to the ISS was relatively stable). The comparison period prior to the pilot, the ISS was 12.62 (scale of 0-75) for n= 281. For the pilot period, the ISS was 11.99 for n= 332."
  • Preparing for presentation to leadership and want to show that difference in LOS is not due to clinical factors like difference in mortality or ISS between retrospective and pilot period. For these purposes, recommend t-test or Wilcoxon test (depending on distribution of data) comparing ISS scores between the two periods, and chi-square test for proportion of deaths during each time period.
  • For eventual manuscript, will need more advanced analyses; recommended going through VICTR for statistical analysis support.

Joseph Kuebker, Endourology

  • "We are attempting to design a study to see if the effective dose (radiation exposure) for a particular type of xray we do is comparable to the generally accepted historical average. Specific questions are how many patients we should enroll to detect differences of >10% (if possible) and what tools to use given we are comparing against a generally accepted number and not against an actual groups of patients/exams."
  • Main "punch" will be plotting the data to describe it. Try boxplots with raw data, perhaps seeing Example 2 here for guidance in Stata: http://www.ats.ucla.edu/stat/stata/code/twboxplot.htm
  • If a test is absolutely necessary, a one-sample t-test (or nonparametric version, depending on distribution of the data) would be most appropriate. But main value of project will be describing how VUMC patients are dosed compared to current guidelines (single number). Recommend minimum of 20 patients per clinical category (eg, female overweight, male normal weight...).

2016 October 12

 

Jessica Grahl, Pharmacy

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  • Basic question: whether antimicrobials are associated with an increased risk of delirium among critically ill patients, using an established cohort (BRAIN-ICU) plus additional data collected from StarPanel.
  • Mental status changes daily and can be normal, delirious or comatose. One idea: multinomial regression looking at status "tomorrow" vs antimicrobial use + covariates "today." How to account for repeated measurements within patients? (Jennifer and Rameela have done cluster bootstrapping; this is complicated and takes awhile)
  • Simpler idea: take out all comatose days from outcome, only look at delirium vs normal status. This limits what we're able to say from the study, but does simplify analyses.
  • Antimicrobials = antibiotics, antivirals and/or antifungals. Patients are often on >1 of these classes and in the case of antibiotics, could easily be on >1 drug of the same class. Also could be lots of interactions between subclasses and other confounders/modifiers.
  • Depending on the type of analysis chosen, this could be a very time-consuming project that might bump it up to the highest level of VICTR projects. If the logistic regression approach (or something similar) is chosen, estimate 90-100 hours for a typical project with data management, manuscript revisions, etc.

Joseph Kuebker, Endourology

  • "We are attempting to design a study to see if the effective dose (radiation exposure) for a particular type of xray we do is comparable to the generally accepted historical average. Specific questions are how many patients we should enroll to detect differences of >10% (if possible) and what tools to use given we are comparing against a generally accepted number and not against an actual groups of patients/exams."
  • Missed clinic

2016 October 5

Debra Braun-Courville, Pediatrics

  • Project is looking at weight gain among adolescents given a specific type of birth control; VICTR application was sent back due to lack of control group. Have access to medical records; suggest matching cases (girls who received a specific type of birth control) to controls (girls who did not receive birth control) as well as possible, using age, race, BMI, and any other available factors.
 

2016 September 28

Luis Huerta, Pulmonary/Critical Care

  • "I am working on a pilot single center cluster-randomized multiple-crossover trial of contact precautions in the Vandy Medical ICU which has not begun yet, and would like assistance with designing the statistical analysis plan, particularly taking into account the cluster-randomization of the planned trial."
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2016 October 12

Debra Braun-Courville, Pediatrics

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Jessica Grahl, Pharmacy

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2016 September 28

Luis Huerta, Pulmonary/Critical Care

  • "I am working on a pilot single center cluster-randomized multiple-crossover trial of contact precautions in the Vandy Medical ICU which has not begun yet, and would like assistance with designing the statistical analysis plan, particularly taking into account the cluster-randomization of the planned trial."
  • Brought up several concerns that have been thought through: seasonality, proximity between patients/presence of infection on floor, how to recruit sites for larger study, different patient populations in different ICUs, admission diagnoses
  • Suggested following up on Frank's email and meeting with him, Dan B, Robert, who have experience with these types of trials

2016 September 21

Ashley Kroeger, Pediatric Critical Care

  • >1000 patients discharged from pediatric cardiac ICU; looking at risk factors for readmission
  • Two different versions of Pediatric Early Warning Signs score: validated, and VCH-specific (extra components)
  • Question 1: is PEWS score, or the difference in PEWS between ICU discharge and floor arrival, predictive of time to ICU readmission?
  • Question 2: do the extra components added at VCH add helpful information when it comes to predicting readmission?
  • Possible analysis: Cox proportional hazards model with outcome = time to readmission (patients who were never readmitted are censored at hospital discharge), covariates = standard PEWS score + score on VCH-specific component + confounders
  • could do two versions of above, one using score at ICU transfer and one using score at floor arrival
  • be wary of multiple hospitalizations per patient - may need to deal with this in analysis or just take first hospitalization per patient
  • estimate ~90 hours for VICTR voucher

Alex Hawkins, Surgery

  • Trying to sort out statistical analysis for three separate cohorts- a group that got radiation pre-op, a group that got it post op and a group that never got radiation from data using the NCDB
  • Question 1 - does neoadjuvant radiation improve rate of R0 resection and/or overall survival?
  • Question 2 - does preop radiation improve rate of survival?
  • Suggest time-varying Cox model to incorporate post-op radiation properly, since patients will start post-op radiation at varying times after surgery (time 0) (including it as a single value would introduce immortal time bias - patients who die faster don't have opportunity to have radiation postop)

2016 September 14

Maya Yiadom, Emergency Medicine

  • Questions about how to phrase results of study with a small sample size and trending-but-not-significant results
  • Key suggestions: make sure to compare patients with and without missing data; try to compare 54 sites with data vs national characteristics (region, hospital type, anything else you can get); discuss limitations of sample size/power, potential bias and overfitting - don't overstate results

Alice Hensley, Pediatric Critical Care

  • "I am working on a project looking at multitasking abilities and comparing scores on an online multitasking test to residency milestone assessments, but would like guidance on how best to analyze the data that I will be collecting. "
  • Out of 100 residents, currently only have data on 22, so priority #1 is getting people to take the multitasking test as soon as possible (needs to be far enough before six-month evaluation to truly be baseline measurements).
  • Get as much data as possible on demographics and resident characteristics, to be able to compare residents who did and didn't take the test and possibly impute for missingness at the time of analysis
  • Probably use proportional odds logistic regression (outcome is a score 1-5)

2016 September 7

Shaun Mansour, med student/global health

  • won't be here until 12:45; see preliminary info in email

Joshua Arenth, Pediatric Critical Care

  • "I am planning a study evaluating the effectiveness of a curriculum on communication skills in the ICU. I would love to talk with someone about project design and potential statistical analysis requirements. "
  • Descriptive statistics will tell most of the story: percentages in each of the three communication styles before and after intervention by intervention group
  • To get p-value, suggest logistic regression: [optimal vs suboptimal style, after intervention] = [style before intervention] + [intervention, yes/no]; odds ratio for intervention is what will tell you whether the intervention group is different from the non-intervention group
  • Spaghetti plot showing change from before to after intervention, with two groups in different colors
  • Estimate about 40 hours for analysis and manuscript through VICTR

2016 August 31

Vance Albaugh & Georgina Sellyn, Surgery

  • Powering a longitudinal study examining cognitive function after surgical (two types) or medical weight loss surgery
  • Could do one/both of the following for a very simplistic approach: paired t-test or two-sample t-test (combined surgery groups vs medical); this should give the "worst case" scenario
  • Feasibility: could easily enroll ~100 patients in each of three groups, cost not a major issue
  • Main interest is whether differences in cognition are seen very early (1 or 3 months) as opposed to the known differences at 12 months after surgery; could do a mixed effects model (longitudinal) for final analysis

Shaun Mansour, medical student/Global Health

  • moved to next week at 12:45

2016 August 17

Chelsea Isom, General Surgery

Uche Anani, Division of Neonatology

  • "I am currently refining my IRB protocol for my mixed method study on clinical decision-making during the perinatal period. I am using a validated survey for my patient population but need some help to determine how big my sample size needs to be to have a power of 80% or p < 0.05."
  • Prospective; planning to administer a decision-making survey to both patients and clinicians (OB/GYN, genetic counselors, and in some cases, neonatalogists) to measure amount of struggle patient is having with making pregnancy-related decision. Main question - how well do clinicians predict how much the patient is struggling. Survey outcome is a continuous score ranging 0-100.
  • For descriptive analyses, not much need for power calculations; get as much data as is feasible, then do (for example) scatterplots and correlation statistics comparing genetic counselors to patients and (separately) OB/GYN to patients.
  • Predictors of a closer relationship are also of interest (education, religiosity, health literacy, years of clinician experience, etc). Could do linear regression for this.
  • Suggest contacting Frank Harrell to determine if there is an available collaboration plan with pediatrics; if not, apply for VICTR assistance. End goal is a manuscript; clinic staff believes this would fit in the <90-hour VICTR category.

2016 August 10

Erin Powell, Pediatric Critical Care

  • Followup visit from 7/13
  • Suggest performing Cronbach's alpha to make sure all questions are informative. Also check to see if original instrument has a validated scoring system. If the alpha works out and there is no other scoring system, suggest creating a single score that is the sum of all 15 questions.
  • Suggested model: post score = pre score + experimental/control (or score = pre/post*experimental/control, using an interaction term to determine whether there is a difference between groups, but first option uses fewer degrees of freedom, and N = 17)
  • Goal is to publish both curriculum and a manuscript about the curriculum's efficacy, probably submitted to an educational journal

Debra Braun-Courville, Pediatrics

  • Followup visit from 2/25
  • Goals: manuscript and conference poster/presentation in March/April 2017 (deadline in November)
  • 282 adolescents (age 12-23) on progesterone-only implanted birth control known to have side effect of weight gain
  • Ideally, see about pulling data from entire population "eligible" to receive this type of contraception (maybe talk to synthetic derivative folks?); that would allow comparison of weight gain/BMI/etc between similar patients who did and did not receive this type
  • If that isn't possible, some ideas...
    • discriminant analysis/risk factor model for weight gain among patients who did get this type
    • separate subgroup analyses among (eg) 12-17yo and 18+yo, because these populations are so different in terms of the outcome
  • For VICTR purposes, actual plan will depend on what data is available, but likely to need 90-100 hours either way for data management, analysis, manuscript writing and revisions
 

2016 July 27

Susan Dickey, Pharmacy

  • She has questions related to a logistic regression for a retrospective critical care research project regarding the duration of antimicrobial therapy for intraabdominal infections in critically ill surgical patients.
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  • 240 patients total who met inclusion criteria, approximately 70 events (event is a composite outcome defining "treatment failure"); excluded transplant patients and those in SI <24 hours
  • Lots of confounding due to ICU/hospital LOS. Suggestions:
  • Look into doing a Cox model for time to event data instead of logistic regression; patients who did not experience event will be censored at hospital discharge
  • Sensitivity analysis only including patients who stayed >=8 days (long enough to potentially be included in the "long" treatment group); this will reduce sample size, but still leaves enough for a reasonable analysis, and would reduce confounding from patients who only stayed a few days
  • Look into whether patients in the long antibiotic group but never got vasopressors were withdrawal of care patients
 

2016 July 20

Melissa Warren, Critical Care Medicine

  • "We have created a new chest x ray scoring system and are seeing how this score can/may be used in patients with critical illness to assess prognostication and outcomes. We have currently scored all of the chest x rays and are analyzing patients in the FACTT database (a former critical care study looking at conservative vs liberal fluid strategies in critical care). I was hoping I could sit down with a biostatistician to discuss which tests would be best to use/how to perform them in SPSS in order to look at the correlation between score/outcomes."
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The Biostatistics Clinic on Wednesdays is dedicated to biostatistics applications in surgery, anesthesiology, and emergency and critical care medicine.
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2016 July 27

Susan Dickey, Pharmacy

  • She has questions related to a logistic regression for a retrospective critical care research project regarding the duration of antimicrobial therapy for intraabdominal infections in critically ill surgical patients.

2016 July 20

Melissa Warren, Critical Care Medicine

  • "We have created a new chest x ray scoring system and are seeing how this score can/may be used in patients with critical illness to assess prognostication and outcomes. We have currently scored all of the chest x rays and are analyzing patients in the FACTT database (a former critical care study looking at conservative vs liberal fluid strategies in critical care). I was hoping I could sit down with a biostatistician to discuss which tests would be best to use/how to perform them in SPSS in order to look at the correlation between score/outcomes."
  • We discussed how to show agreement with continuous measures (Bland-Altman plots) and recommended she create plots for the overall score and by component to see if any portion of the score is driving any disagreement. If needed, we also recommended looking by quadrant because of potential issues in the lower left quadrant.
  • Due to the scope and technical complexity of her questions, we recommended that she check with Tatsuki Koyama (tatsuki.koyama@vanderbilt.edu) to see if this work would be covered under a collaboration plan. Otherwise, we encouraged her to apply for a VICTR voucher.
  • Some of the outcomes of interest are time to successful extubation, ventilator-free days, time to death, LOS, etc. We think the scope of the work should take at least 90 hours.

2016 July 13

Erin Powell, Pediatric Critical Care

  • They would like to discuss the data for their project evaluating the effectiveness of a curriculum to teach communication skills to pediatric critical care fellows.
  • We discussed the different measures they are using to evaluate their chosen outcomes and appropriateness of methods suggested to use for analysis (t-tests).
  • We suggested factor analysis might be most appropriate to answer their questions of interest and suggested that if this approach is beyond the scope of their abilities to apply for a VICTR voucher.

M. Frances Wright, Medical student

  • They have questions related to their project on blood product utilization during liver transplant surgery.
  • They had several questions related to outliers and what to do with them in the analysis. We highly encouraged them not to remove them from any analysis unless they can prove that these measurements were made in error. We also tried to help them understand analyses done for them previously. We also encouraged them to consider applying for a VICTR voucher if the scope of the future analyses seems beyond their abilities. They were going to check whether a collaboration existed between our two departments.

2016 June 29

Justin Gregg, Urology

  • He has questions regarding sample size calculations.

Jamie Felton, Pediatric Endocrinology

  • "My questions are regarding the best way to statistically analyze a data set from an ELIspot assay."

2016 June 8

Ravi Bamba, Plastic Surgery

  • "I have a dataset that I finished collecting from a previous project. I needed help running my stats but I do not have funding."
  • Investigating risk factors for recurrence of pressure sores in subjects who had a surgical intervention to fix the problem.
  • He has data from 1997 - 2015. We advised that he restrict follow-up to ensure that all have had equal opportunity to have a recurrence observed.
  • We recommended that he use non-parametric tests (Wilcoxon Rank Sum test, e.g.) for the univariate analyses.
  • His primary question of interest is whether there is a difference in time to recurrence between different pre-specified risk factors. We showed him the UCLA website as a resource as to how to fit Cox models in SPSS. We also advised him to organize the covariates of interest from most to least important based on clinical knowledge and literature.

2016 June 1

Ravi Bamba, Plastic Surgery

  • "I have a dataset that I finished collecting from a previous project. I needed help running my stats but I do not have funding."
  • No show for clinic

2016 May 25

Deborah Jacobson, General Surgery - canceled due to OR schedule

  • "We have data including complications/pt/year for 10 years of data and want to see if there is a significant decline in complication rates over time."

Viraj Mehta, Ophthalmology - moved to Thursday clinic

  • "I'm evaluating eye motility outcomes after surgery for orbital floor fractures in children. I have collected all the data, and needed help figuring out the best way to analyze it."

2016 May 18

Vance Albaugh, Department of Surgery

  • "I have a question about powering a clinical research study, as well as some specifics about the data analysis."
  • Looking at gastric bypass patients' glucose tolerance tests at multiple time points after surgery. Plan: give them a regular glucose tolerance test, measure response (every 15-30 minutes, so we get a curve for each test), then a few days later give the same test supplemented with salt. Hypothesis is that salt will make the glucose response worse (higher) initially, but by a year after surgery, the salt/no salt responses will be roughly equivalent. No-salt response will also change over time as patients become less insulin resistant.
  • Of possible interest: DeLong et al, 1988: http://www.ncbi.nlm.nih.gov/pubmed/3203132
  • Jeffrey Blume in biostats might be a good resource for AUC curves - this project is especially complex due to longitudinal measurements + AUC measurements
  • Number of patients could be pretty high - this is a relatively easy study to do compared to other gastric bypass studies
  • Describing and plotting this data will likely be as or even more informative than statistical testing. Something like one row per patient, one panel per time point, with salt vs no salt at each time point in each panel. 5-10 patients' worth of pilot data would be highly informative for future sample size/analysis discussions.
  • Maybe a mixed effects model along the lines of: glucose response = salt * time + visit * time + covariates (# covariates restricted by sample size)
  • Jackie is looking into latent growth curves

Maya Yiadom, Emergency Medicine

  • I am submitting a K23 proposal and could use help identifying:1) Whether Iíve selected the right study design for may aims; 2) The right analysis method should be for Aims 2 and 3; 3) How do I get an appropriate ED (Aim 2) and patient (Aim 3) sample size for Aim 2 and 3?
  • Could fit one model using patient-level data to answer both aims 2 & 3, including both ED- and patient-level characteristics. This would allow you to get estimates for, say, academic vs non-academic institutions, or patient age, after adjusting for all other factors.
  • Recommend plotting time to diagnosis & time to treatment for any available pilot data to help inform model choice. If outcome is normally distributed and patient-level data is used, a linear mixed effects model could be good (site is random effect).
  • Time to treatment gets very tricky because some patients get treated via medication and some via procedure - procedure inherently has longer time to treatment. Look at distribution of times separately and together - will likely need two separate models to answer treatment question.

2016 May 11

Jordan Rupp, Emergency Medicine

  • "I have a couple quick statistics questions for a small QA study in which I am participating. We will be assessing the lung ultrasound abilities of the emergency medicine residents at the Nepali hospital after a brief 2 week teaching session given by Bales and I in March. I need some help making sure our sample size calculations, etc. are correct."
  • Studying pneumonia; typical gold standard is CT, but not feasible in Nepal
  • Original plan: do chest x-ray (standard, but can take up to six hours) and ultrasound on all suspected pneumonia patients, compare to discharge diagnosis
  • No data from before the class is available, so can't compare pre- and post-training. If that is the true main question of interest, the study needs to involve equivalent providers or sites who didn't get the training course.
  • Possible main question: does doing the ultrasound at presentation add value to the standard chest x-ray, in terms of accurately predicting whether the patient is diagnosed with pneumonia?
  • Need to refine research question before continuing with sample size calculations.
  • If money is available to do CTs on everyone, sample size will depend expected sensitivity/spec/PPV/NPV and on how wide a margin of error would be clinically acceptable (eg, if we expect something like a point estimate of 80%, would a 95% CI of [70%, 90%] acceptable?)

Amelia Maiga, General surgery resident

  • "I have two specific R coding questions for a survival analysis I'm doing on a multi-institutional retrospective cohort of surgically-resected distal cholangiocarcinomas. I've tried stack overflow and perusing the Hmisc source code without success.
  • 1. I am using aregImpute to impute missing covariates, but run into errors when I attempt to include any factor variables with 6 or fewer observations per factor level. When I attempt to specify group=d$site (where site is a factor covariate with 10 levels, one of which only has 6 observations), I get a different error message about not all values of d$site represented in observations with non-missing values of another covariate.
  • 2. I would like to use fit.mult.impute to fit a Cox proportional hazards model utilizing the data imputed by aregImpute, but despite specifying the xtrans object appropriately, I keep getting an error message "imputed=TRUE was not specified to transcan", suggesting that R thinks I intend to use transcan rather than aregImpute to impute the data to fit the model."
  • Suggest an interaction term between log10(followuptime) * death in the aregImpute() (might need to create log variable beforehand)
  • Suggest pooling sites by region in a new variable to use in imputation, and/or include site in analyses, possibly by stratifying (strat = 'site' in cph()), which would allow the baseline hazard to differ between sites (still get one HR for each covariate)

2016 May 4

Drew McKown, Pulmonary/Critical Care

  • Hoping to discuss test selection/power calculations prior to IRB submission
  • "The idea is to perform a physiologic assessment of the patient to determine an ideal ventilator setting and then assess if that setting is different from one prescribed by an algorithm."
  • Basic question: There is an ARDSNet algorithm for setting tidal volume based on PEEP. Want to compare the tidal volumes recommended by the algorithm by tidal volumes determined by stress index (measure of how much stress is on the lungs).
  • Recommendation - calculate power/sample size using a paired t-test and SD of the difference for the means between ARDSNet/stress index results. However, for actual analysis, recommend nonparametric (Wilcoxon) test, since especially with a small sample size, assumptions for t-test are likely to not be met.
 

2016 April 27

Andy Brooks, Center for Human Genetics Research

  • Wants to discuss power calculations for proposed research project
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2016 April 27

Andy Brooks, Center for Human Genetics Research

  • Wants to discuss power calculations for proposed research project
  • No current power methods exist for these genetics methods - gave some suggestions about simulations, etc. Suggest coming to future Tuesday omics clinics for more long-term discussions (except first Tuesday of the month).

2016 April 20

Vance Albaugh, Surgery

  • "I am planning a clinical study and would like my sample size to be reviewed by a biostatistician before I submit for VICTR funding. The study is a randomized, double-blind experiment in human volunteers examining the effects of a drug commonly given to liver failure patients on oral glucose tolerance."
  • First study: ileostomy patients, want to see if they respond differently to placebo vs. treatment given directly to the small intestine. Patients with and without diabetes will likely respond very differently - need to know how to power this (for VICTR application).
  • Most conservative approach - power separaately for diabetic and non-diabetic patients. Complication is that there is no pilot data on diabetics; can guess that variance is twice as much for these patients as non-diabetics. For analysis, suggest doing one model with interaction term to get most efficient/accurate treatment effect (AUC = tx + diabetes + tx*diabetes).
  • Second study: bowel length vs. weight loss (and potentially other outcomes) in gastric bypass patients. Suggest longitudinal approach: model with patient ID as a random effect, with weight at each time point as the outcome and baseline weight and bowel length and time as independent variables. Data will be structured with multiple records per patient. Can show results graphically by showing a line over time for patients at (for example) the 25th, 50th, and 75th percentile of bowel length. Allow at least bowel length to have a nonlinear association with the outcome (restricted cubic splines is a popular approach). Could apply for VICTR voucher if this analysis is too complex to do himself.

2016 April 13

Flavio Silva, Orthopedics

  • Project: Scapular and cervical neuromuscular deficits in musicians with and without playing related musculoskeletal disorders (case-control study)
  • Asked for help with regression and descriptive statistics
  • Case-control study - original plan is to match; we suggest using entire cohort and adjusting for confounders (effective sample size of ~70)
  • Outcome: chronic pain; covariates: three test scores (two are closely related, one is less closely related)
  • One test: six unique values (20, 22, 24, 26, 28, 30; he has dichotomized this based on previous literature); neck flexion: number of seconds (less than a minute; normative means are 24 or 38 depending on gender - might dichotomize this); scalpular dyskinesis is dichotomous yes/no
  • Suggest looking at Spearman correlation between two neck flexion tests to see how closely related they are - if very closely, might not make sense to include both (adjusting for one would make the other meaningless)
  • Additional analyses use test scores (above) as dependent variables. For linear regression with test with 20, 22... as outcome, need to carefully look at diagnostics to make sure results are reliable. Some guidance for SPSS might be here: http://www.ats.ucla.edu/stat/spss/webbooks/reg/chapter2/spssreg2.htm. If assumptions aren't met, could consider ordinal logistic regression for this outcome.

Tony Qiu, Anesthesiology

  • "I'm doing a research with anesthesiology department and currently in process getting IRB approval, I have a few questions regarding data analysis part. My question is what model can I use to assess mortality data across different institutions?"
  • 30-day mortality is often used as a quality marker for non-emergent surgeries. Question is whether institutions are "gaming the system" - keeping patients alive long enough to make that marker, then transferring to palliative care, or just not taking more severe cases due to the risk of not making that marker.
  • For IRB application, could do Kaplan-Meier plot for general time to death across all institutions. This does not get at the question of whether different institutions are "gaming the system" (keeping patients alive to the 30-day marker and then being less careful).
  • But one K-M plot is not going to fully answer the question (might get IRB approval, but won't actually answer the question).
  • When it comes time for the analysis, might suggest a Cox proportional hazards model with time to death as the outcome, could adjust for potential relevant confounders (severity of case, etc).

2016 March 30

Flavio Silva, Orthopedics - Canceled

  • Project: Scapular and cervical neuromuscular deficits in musicians with and without playing related musculoskeletal disorders (case-control study)
  • Asked for help with regression and descriptive statistics

2016 March 23

Ravi Bamba, Plastic Surgery

  • Working with burn patients, looking for association between age and a) number of cytokines and b) % change in 2nd vs. 3rd degree burn between initial and final assessments.
  • Initial plan was to collect data only on patients <30 and >65 years old, then to look for a difference in the two groups. We recommended collecting data on a spectrum of patients and then looking for an association between patient age and the two outcomes. This will allow the results to be more generalizable and have more power (less loss of information). Hoping to collect data on ~60 patients if time/logistics allow.
  • Also consider what confounders to collect data on and adjust for: possibly total burn surface area, comorbidities, burn mechanism, other clinical factors.
  • Plan to apply for VICTR voucher for both lab funding and statistical support, with end goals of pilot data for a grant and a manuscript. Suggest around 60-75 hours of statistical support for data management, modeling and diagnostics, manuscript writing/editing and revisions.

2016 March 16

Lyly Nguyen, Critical care

  • Comparing burn ICU outcomes from time period before a specific drug was administered for inhalation injury (2002-2008) and after that drug became part of standard care (2008-2014).
  • For univariate comparisons, recommend describing variables using median and interquartile ranges (rather than or in addition to mean/SD) and using Wilcoxon nonparametric tests rather than t-tests.
  • Outcomes include ICU LOS, probably hospital LOS, vent-free days, and pneumonia (ever/never during ICU stay).
  • For continuous outcomes, all of these are highly skewed, so need to transform them before running a linear regression model (see this link for help in SPSS: https://statistics.laerd.com/spss-tutorials/transforming-data-in-spss-statistics.php).
  • Number of variables you can put in the model: For continuous outcomes, it's the number of complete cases (no missing data) / 10-20. For pneumonia, it's the minimum of (pneumonia, no pneumonia) / 10-20.
  • Calculating vent-free days: Pick a common denominator among all patients (say, 28 days). If a patient dies, they automatically get 0. If they survive, they get (28 - number of days on vent in first 28 days of ICU stay; assume not on vent after ICU discharge).
  • Missing data methods in SPSS may not be robust.
  • Limitations/things to be aware of:
  • - Missingness can strongly bias results and affect number of covariates that can be included in the model.
  • - Mortality rate is about 20% and can also bias results - as one example, this may mean that patients with a shorter ICU LOS are actually doing worse (dying earlier) than patients with a longer ICU stay.
  • - Temporal confounding can limit interpretation - can't say that lower pneumonia rates cause fewer vent days, for example, since we don't have timing of either event; treatment effect is also confounded by time. Also clinical care may have changed fairly drastically over the 12-year study period.

2016 March 9

Oliver Gunter, General Surgery

  • "I have a question regarding a large database study Iím conducting. This is an IRB approved study that Iím trying to finalize for submission for publication. I have some questions regarding possible propensity score matching to eliminate problems I have with differences in patient characteristics."
  • Given that this is survey weighted data, and there is plenty of sample size (N = 186,000 with event rates of 10-15% for the two outcomes), there doesn't seem to be a need for propensity score adjustment, and it could add complications due to survey weighting. Just adjust for individual covariates in the main model.

2016 March 2

Scott Boyd, Surgery

  • Main research question: whether short (24h) vs. long-term (7-day) antibiotic use is associated with a difference in infection rates after a specific type of oral surgery at two sites (retrospective cohort). Most infections concentrated within 30 days of surgery date; infections observed after this tend to be different and in different types of patients.
  • Major limitation: antibiotic use is constant across each site, so antibiotic duration is completely correlated with study center. Suggestion: describe rates of other types of infection at those sites to (hopefully) show that those are similar, so that any association found in this analysis is more likely to be due to antiobiotic duration than just study center effect.
  • Suggested logistic regression (outcome = infection, yes/no) and also Cox proportional hazards model, where outcome = time to infection. No patient has >1 infection. In either of these models, effective sample size is ~53 (number of infections), so could adjust for up to five parameters to account for potential confounding.
  • Make sure to discuss in limitations section the idea that despite doing everything we can, it is not possible to completely tease out the association of antibiotics vs. the association of study center and unmeasured confounders that go along with that.
  • Planning future prospective study which will hopefully better address these issues.
  • For VICTR planning purposes, this should fit in a regular 90-hour VICTR project.

2016 February 24

Debra Braun-Courville, Pediatrics

  • She is working on a clinical research project looking at contraceptive usage among adolescents from chart review data and needs guidance regarding the analysis.
  • Recommend doing KM curve for up to 12 months (or whenever a large proportion of patients have data up to this point - majority of patients had device inserted >x months ago).
  • Could do a Cox proportional hazards model with time to removal as outcome (patients with device still in are censored at 12 months, or whatever time point is used), and baseline variables as covariates: age, previous pregnancies, etc.
  • For variables such as bleeding, weight gain, etc which are collected during followup, recommend doing descriptive statistics for reason the device was removed - analysis with these variables is going to be biased due to lots of missingness in the clinical record.
  • Try looking at UCLA's stats web site for examples in SPSS, or apply for VICTR voucher

Dan Wang, Hematology-Oncology fellow

  • "Iím a first year Hem-Onc fellow and am doing an epidemiology project and had a quick question on how to calculate a p-value for comparing two APC (annual percentage change) using data from a SEER-like database (Texas Cancer Registry)."
  • Because the data is very aggregated (rates per year by demographic), not much we can do statistically. Descriptives and figures are probably the best bet.
 

2016 February 17

Meredith Stocks, Medical student

  • "I am a medical student assisting Dr Sarah Krantz with a project looking at short interpregnancy interval and counseling at antepartum and postpartum appointments. We already have a population of short IPIs but need help setting up our control group. Dr Krantz has done a bit of work regarding the design of the project and I will email specifics closer to the date of the clinic as we are meeting this week to have everything ready."
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  • Have 300+ women with short IPIs within five years; need to know how many cases to pull. Ideally, pull all data within same five-year period, but if this isn't feasible due to logistics, try PS software to see how many patients give adequate power. http://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize#PS:_Power_and_Sample_Size_Calculation
  • Perform one logistic regression model: short IPI = attendance at antenatal visit + confounders (demographics, provider type, etc). Interpretation: odds ratio for antenatal visit is the odds of short IPI for those who attended antenatal visit vs. those who didn't, adjusted for all other confounders.
  • Matching is also a possibility; can be more clinically straightforward, but is more work on the front end.

Clint Leonard, Vanderbilt Burn Center

  • "We are a team from the Burn Center currently working on a manuscript entitled "Assessment of Outreach by a Regional Burn Center: Utilization of resources should be part of education for referring providers." We had some questions about analysis and interpretation of our results that we were hoping to discuss with you this Wednesday. "
  • Essentially, we studied all interfacility transports to the Burn Center from Jun 2012 - Jul 2014. For the 623 patients that met our inclusion criteria, we recorded:
             Method of transport (helicopter, airplane, or ambulance)
             Burn Size
             Outside hospital estimate of burn size
             Actual burn size (as determined by our burn attending)
             Burn Mechanism
             Fluid resuscitation data including fluid type, rate, and bolus administration. 
             Intubation status
             Length of stay (both ICU and total hospital) 
            The difference between our estimate of burn size and outside hospitals' estimates
            Trends in fluid resuscitation rates
            Trends in air versus ground transport
         
  • As this is a retrospective study we are only identifying trends rather than making sophisticated inferences, so the majority of our findings are simple declarative statements such as "Of 143 patients who arrived by air, 18 (13%) and 49 (31%) were discharged from the hospital within 24 and 48 hours, respectively." However, there are a few areas that I would appreciate your input on:
  • Is there a good way to represent the relationship between overestimation of TBSA and overresuscitation? What is the best way to see which demographic factors (age, TBSA, mechanism) affect the likelihood of air vs. ground transport? Similarly, what is the best way to see which demographic factors (age, TBSA, mechanism) affect the likelihood intubation? - suggested boxplots of delta(TBSA) for each overresuscitation group
  • Related to the above, what information will we glean from a chi square test that we will not get from a logistic regression, and vice versa? Would it be worth it to perform both? - logistic regression allows adjustment for confounders, and gives direction and magnitude of association. Chi square only gives association and does not account for confounding at all.
  • I want to doublecheck the validity (and utility) of making certain statements without controlling for other variables, e.g. "18% of patients who were burned while smoking on O2 died, while all other mechanisms combined had a mortality rate of 4%" - definitely need to adjust for confounders in this case, since patients with this burn mechanism will have inherent differences from overall population. Use logistic regression if you have certain death data on everyone, or if you only have (for example) in-hospital death, could use Cox proportional hazards regression and censor at hospital discharge.
 

2016 February 10

Jin Han, Emergency Medicine

  • "I have a cohort of delirious and non-delirious patients (230 patients). I want to preliminarily develop novel subtypes of delirium based upon clinical and biomarker data."
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  • Have data on several delirium characteristics (severity, arousal level, etiology) on 228 patients. Have functional outcomes at 6m on ~160, cognitive outcomes on ~110, and mortality rate of ~30%. Interested in risk prediction score for outcomes using delirium characteristics (whether or not patient meets criteria for full delirium) as well as patient characteristics.
  • Planning to submit R01 to develop the full risk score. Suggested VICTR design studio with clinical + statistical experts to figure out how best to use this pilot data in grant submission.
 

Justin Godown, Pediatric Cardiology

  • The project is development of risk prediction models for placement of a ventricular assist device vs medical management with outcomes of survival to transplant and 1 year post transplant survival in pediatric patients. Considering using propensity matching due to variability within groups.
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  • Main goal would be to develop a risk prediction score for mortality, with VAD vs. medical management as a key component
  • Data comes from two databases with a wide variety of cardiac patients; suggest limiting patients included to those who are sick enough where this decision would have to be made.
  • Could do a Cox regression model with time to death = baseline factors + VAD vs. medical management; not sure about getting a risk probability from this, though.
  • Could also do a logistic regression model with, say, one-year mortality as outcome; more straightforward to get a probability, but lose the time information.
  • Propensity scores could be useful here to either match VAD patients with medically managed patients with similar propensity of VAD, or as a data reduction technique if number of events is low. It's possible that neither of these are necessary.
 

2016 February 3

Jin Han, Emergency Medicine

  • "I have a cohort of delirious and non-delirious patients (230 patients). I want to preliminarily develop novel subtypes of delirium based upon clinical and biomarker data."
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 I am looking for guidance on how to proceed with performing a validation of the bedside swallow screening used for acute stroke patients in the ED, neuro ICU, and the neuro care unit.
The project is development of risk prediction models for placement of a ventricular assist device vs medical management with outcomes of survival to transplant and 1 year post transplant survival in pediatric patients.  Considering using propensity matching due to variability within groups.
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 I am looking for guidance on how to proceed with performing a validation of the bedside swallow screening used for acute stroke patients in the ED, neuro ICU, and the neuro care unit.
The project is development of risk prediction models for placement of a ventricular assist device vs medical management with outcomes of survival to transplant and 1 year post transplant survival in pediatric patients.  Considering using propensity matching due to variability within groups.
I am planning a clinical study and would like my sample size calculations to be reviewed by a biostatistician before I submit for VICTR funding.
The study is a randomized, double-blind, experiment in human volunteers examining the effects of a drug commonly given to liver failure patients on oral glucose tolerance.
 

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2016 February 17

Meredith Stocks, Medical student

  • "I am a medical student assisting Dr Sarah Krantz with a project looking at short interpregnancy interval and counseling at antepartum and postpartum appointments. We already have a population of short IPIs but need help setting up our control group. Dr Krantz has done a bit of work regarding the design of the project and I will email specifics closer to the date of the clinic as we are meeting this week to have everything ready."

2016 February 10

Jin Han, Emergency Medicine

  • "I have a cohort of delirious and non-delirious patients (230 patients). I want to preliminarily develop novel subtypes of delirium based upon clinical and biomarker data."

Justin Godown, Pediatric Cardiology

  • The project is development of risk prediction models for placement of a ventricular assist device vs medical management with outcomes of survival to transplant and 1 year post transplant survival in pediatric patients. Considering using propensity matching due to variability within groups.

2016 February 3

Jin Han, Emergency Medicine

  • "I have a cohort of delirious and non-delirious patients (230 patients). I want to preliminarily develop novel subtypes of delirium based upon clinical and biomarker data."

Kiersten Brown Espaillat, Emergency Medicine

  • "I am looking for guidance on how to proceed with performing a validation of the bedside swallow screening used for acute stroke patients in the ED, neuro ICU, and neuro care unit."
  • Wants to assess validity of a VUMC-developed bedside swallow assessment compared to video fluoroscopy among stroke patients who can safely have swallow assessment. Have retrospective data on patients who failed swallow screen (thus required fluoroscopy), but no fluoroscopies currently on patients who passed swallow screen.
  • Sample size needed will be determined by confidence interval width that's clinically meaningful - for example, if point estimate is 90% sensitivity but CI goes down to 75%, is that clinically OK, or is that too low? Kiersten will look up validation study for only validated tool and use that as a starting point.
  • Swallow tool failure rate is ~15%.
  • VICTR could be a good resource.

2016 January 13

Mike LeCompte, Surgery and Critical Care

  • Mike wants further input on his application to VICTR regarding a surgical resident education project.

2015 December 16

Jason Singer, Medical student

  • "I have a few quick questions for biostats clinic related to how to treat data from Likert scales. For example, if the average choice is somewhere between agree and strongly agree or if the average choice is between
usually and always, how do I report a mean and standard deviation?"
  • Due to limitations with REDCap on tablets, their survey had to be restructured from using VAS's to categorized answers. Because of this, the type of analysis required to answer their questions of interest has changed.
  • Doing a chi-square test will determine if there is a difference in distribution of responses between inpatient and outpatient groups. If there is a significant difference, then differences in proportions could be done in which a specific cut-off is determined.
  • Another alternative is to fit a proportional odds model with the responses as the outcome and group (inpatient versus outpatient) as the main covariate. The advantage to this approach is that confounders can be included in the model. The proportional odds assumption needs to be tested once the model is fit.

2015 December 9

Mike LeCompte, Surgery and Critical Care

  • "I am trying to design a study on surgical resident education techniques and wanted to get some input on my study design and setting up my statistical methods."
  • We would recommend doing the pre-video at the first junior surgical experience to then compare to the post-video after all surgeries are complete. We would also recommend having the same number of assistant surgeon experiences between the two groups if the pre-video is made at the time we recommend.
  • We recommended that he determine what would be clinically meaningful differences in the different outcome measures to help determine what power he would have given his fixed sample size.
  • We think it will require about 40 hours of statistical support to do the analysis and help in manuscript preparation.

Tommy An, Medical Student

  • Question about Stata output for logistic regression

2015 November 25

Renee Hill, Physical Medicine & Rehab

  • Postdoc in Center for Integrative Medicine; interested in seeing whether an 8-week intervention results in reduced emotional distress, and whether this reduction is different depending on level of self-compassion. No control group; all patients received the intervention, and have pre- and post-intervention distress scores.
  • For main question (reduction in scores), could do a paired t-test or (more likely) a Wilcoxon signed rank test, if data is not normally distributed, comparing pre- and post-intervention scores.
  • To see whether the association of pre- and post- scores differs based on level of self-compassion (continuous value), could do a multivariable regression model: post-intervention score = pre-intervention score + self-compassion score + (pre-score * self-compassion). Can also add potential confounders (age, gender, etc) to this model. Linear regression is most common, but need to check distribution of the outcome first - if the outcome is not normally distributed, this model may not be appropriate or reliable. Also check model assumptions, such as residual vs. fitted plots.
  • Secondary question: do post-intervention scores stay stable in the months after the intervention, or rebound? Data available on cohorts from 2012 through 2015, all with different followup times. Could do a regression model with followup (current) score as the outcome, months since completion of intervention as the main exposure, and adjusting for other confounders (anger score, age, etc).
  • Suggested looking into a VICTR voucher (check Starbrite for more info).

2015 November 18

Michael Ghiam, medical student - canceled

  • "Hello, I am a third year medical student working on a epidemiology study using Stata to analyze my data. I am currently working on writing code for my study and I am running into some roadblocks. I was wondering if I could come in on Wednesday or Thursday and get some pointers about which codes would be best to use and if Iím analyzing my data the right way. Please let me know if this is at all possible and what I should provide you with in advance."

Tommy An, medical student

  • "I have attempted a logistic regression in STATA to predict whether a patient has MRSA or MSSA musculoskeletal infection based on presentation data from the emergency department. I need some advice to see if I'm on the right track with my statistical analysis."
  • Logistic regression seems fine. Make sure to note that results will only be generalizable to patients who actually did develop MRSA or MSSA, not patients who developed neither.

Jamie Robinson, Surgery

  • " I am trying to do a Kmeans analysis and having some difficulty with figuring out if what I am getting is what it should look like. "
  • Suggest looking at varclus() function in Hmisc package to cluster variables instead of patients
  • Also could create principal components (using princomp()) before clustering to reduce data to two dimensions before using kmeans
 

2015 November 11

Nick Kramer

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  • "...here is our project and question:
literature review of weight bearing after posterior acetabular fracture. The data available is relatively limited looking at the question we are asking so we are attempting to combine several studies to see if there is a trend for benefits of early vs late weight bearing. We have several questions regarding the best way to do this, if it is possible at all."
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  • "...here is our project and question: literature review of weight bearing after posterior acetabular fracture. The data available is relatively limited looking at the question we are asking so we are attempting to combine several studies to see if there is a trend for benefits of early vs late weight bearing. We have several questions regarding the best way to do this, if it is possible at all."
  • Clinic statisticians will email the department to ask if anyone has further expertise in this area. This study presents a challenge because there is no common exposure in each study included in the review; rather, each study is a cohort of either early or late walking patients. Raw data/SDs are also available on very few of the studies.
 

Michael Benvenuti, Orthopedic surgery

  • "I am working on a pilot study to determine the effect of antibiotics on length of stay and culture sensitivity in pediatric musculoskeletal infection. i have done some preliminary analysis using Stata and have a few questions about how to continue."
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  • We discussed logistic regression for the main outcome: positive culture = covariates. The number of covariates that can be reliably put in the model is roughly equal to [minimum of positive/negative cultures] / 10-15. So, if the split is 50/50 and there are 120 patients, 60 "events" / 10 = 6 possible covariates in the model.
  • For length of stay, we suggested using a Cox model looking at time to hospital discharge, rather than using LOS as a continuous outcome. (LOS generally has a distribution that is difficult to model.) All covariates for this model are measured at ED presentation.
  • About 30 patients are included in the cohort but have no culture measurement. It is important to look at whether these 30 patients are different from the other 120 - was no culture done because they were sicker/less sick, younger, etc.
 

2015 November 4

Courtney Baker

  • "In terms of the project, it is a collection of data on intra-operative coagulation factors and transfusion data for pediatric scoliosis patients over the last 3 years. I have asked a number of questions around "what determines/predicts intra-operative blood loss in these cases?" I have done rudimentary (and most likely not entirely correct) statistical analysis between the coagulation factors and the transfusion results. What is needed is a rigorous discussion about employing multivariant statistical analysis on the associations I see. The goal of this data set is to publish some new observations and associations in order to develop a quality improvement protocol AND a more rigorous research project into one or more of these specific associations."
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 I am looking for guidance on how to proceed with performing a validation of the bedside swallow screening used for acute stroke patients in the ED, neuro ICU, and the neuro care unit.
The project is development of risk prediction models for placement of a ventricular assist device vs medical management with outcomes of survival to transplant and 1 year post transplant survival in pediatric patients.  Considering using propensity matching due to variability within groups.
 
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2015 November 11

Nick Kramer

  • "...here is our project and question:
literature review of weight bearing after posterior acetabular fracture. The data available is relatively limited looking at the question we are asking so we are attempting to combine several studies to see if there is a trend for benefits of early vs late weight bearing. We have several questions regarding the best way to do this, if it is possible at all."

Michael Benvenuti, Orthopedic surgery

  • "I am working on a pilot study to determine the effect of antibiotics on length of stay and culture sensitivity in pediatric musculoskeletal infection. i have done some preliminary analysis using Stata and have a few questions about how to continue."

2015 November 4

Courtney Baker

  • "In terms of the project, it is a collection of data on intra-operative coagulation factors and transfusion data for pediatric scoliosis patients over the last 3 years. I have asked a number of questions around "what determines/predicts intra-operative blood loss in these cases?" I have done rudimentary (and most likely not entirely correct) statistical analysis between the coagulation factors and the transfusion results. What is needed is a rigorous discussion about employing multivariant statistical analysis on the associations I see. The goal of this data set is to publish some new observations and associations in order to develop a quality improvement protocol AND a more rigorous research project into one or more of these specific associations."
  • Recommend doing multivariable regression models instead of univariate analyses: eg, "end of surgery platelets = baseline platelets + other baseline variables". Specific regression type depends on distribution of outcome; linear regression is appropriate for truly continuous outcomes with wide enough range (eg, 0-100) as long as assumptions are met (eg, residuals are normally distributed). Logistic regression is appropriate for dichotomous outcomes, and something like proportional odds logistic regression would be appropriate for integer values with small ranges (eg, 0-3).
  • Suggested spaghetti plots to describe fibrinogen loss during surgery, one line per patient, with reference line at time of fibrinogen intervention.
  • Models could be fit using restricted cubic splines for continuous covariates, such as baseline fibrinogen.
  • Discussed multiple comparisons, which GraphPad uses by default.
  • Use nonparametric tests of association (such as Wilcoxon rank sum test/Mann-Whitney) unless it is known that variable is normally distributed (and even then, nonparametric is a safe choice).
  • Possibly talk with Shirley Liu about doing regression analyses due to ortho collaboration; otherwise apply for 90-hour VICTR voucher.

2015 October 28

Emily Buttigieg, Medical student

  • "I am a medical student (VMS III) working on a research project in the Pediatric Surgery department. My project involves measuring body composition using tissue resistance and reactance measurements and comparing it to standard measurements, BMI, weight and height. I have collected my data and am inquiring as to the best software and approach to analyzing my data. I was hoping to attend next Wednesdayís clinic. Thanks in advance for your help. "
  • About 30 patients total with repeated measurements. Suggested using Spearman correlations on Z-scores from device and BMI calculations, using a) only one measurement per patient and b) all measurements per patient. Bland-Altman plots might also be helpful, and scatterplots of raw data will be very useful.

2015 October 21

Mary Bayham, Global Health

  • We seek to describe the burden of fever, diarrhea, and respiratory illness among children aged 6-59 months in Zambťzia Province, Mozambique as well as predictors (individual and system level) of health care utilization for these children. The goal of this thesis is to identify significant predictors (individual and system level) of healthcare utilization for children under five with fever, diarrhea, and cough. These findings could inform future planning, policy and interventions in reducing under five morbidity and mortality in Mozambique.

  • Dataset includes 3,800 families; 2,700 children < 5-years-old; and 14 districts (3 of which were oversampled).
  • Suggest reporting descriptive statistics for 3 individual districts and comparing to all other districts combined.
  • Suggest utilizing a multivariable logistic regression model for healthcare utilization - initially without any weighting and once more weighting for district - and comparing results.

Erin Hamilton, Global Health

  • Goal to assess impact of a nutrition education intervention in children.
  • Outcome is Z-score BMI adjusted for age measured at 4 time points. Dataset includes 151 children with Z-score BMI measured at least twice.
  • Planning to use multi-level mixed effects linear regression model adjusted for gender.
  • Suggest utilizing paired Wilcoxon test for change in Z-score BMI between time points (pre vs. post) and box-and-whisker plot of median and IQR at each time point.

2015 October 14

Rachel Hayes, Surgical Sciences

  • "I am struggling to interpret an interrupted time series using logistic regression and proc glm in R. Iíve attached a de-identified data set (date is shifted) and some R code."
  • Difference in interpretation between lrm() and glm() models is due to differences in default anova() tests - anova.rms() uses added last tests, while anova.default() uses sequential tests (eg, time after adjusting for only variables ahead of it in model formula).

2015 October 7

Thomas An, Medical student

  • "I am in Dr. Schoeneckerís lab studying musculoskeletal infection. I have outcomes data and numerous variables for patients with musculoskeletal infection and am hoping to set up a multivariable analysis to predict which variables are most predictive of outcomes."
  • We discussed linear regression and diagnostics to use to evaluate whether assumptions for linear regression have been met (residual/predicted plots). We also discussed potential alternatives if the assumptions for linear regression are met including transformations, ordinal regression, or negative binomial regression.

Teerayut Tangpaitoon, Department of Urology

  • "Our study is about evaluate outcome of Holmium laser enucleation prostate surgery (HoLEP) compare to HoLEP with concurrent Cystolitholapaxy in same setting(retrospective)."
  • The two groups are completely confounded by presence of bladder stones. Those who received the additional therapy were patients with bladder stones.
  • We discussed t-tests versus Wilcoxon Rank Sum tests. We also discussed using linear regression to adjust for potential confounders.
  • We recommended the UCLA website for help in how to perform the analyses. ( http://www.ats.ucla.edu/stat/spss/ )
 

2015 September 30

Kelly Maguigan, Critical Care Pharmacy

  • Project involving enteral intolerance in spinal injury patients who are on concurrent vasopressors.
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  • Planning to request VICTR voucher for analysis.
  • Retrospective chart review of 80-100 patients from TRACS registry who were on pressors + enteral feeding for at least one hour.
  • Primary analysis: risk factors either count of enteral intolerance during hospital stay (Poisson or negative binomial model?), and/or daily yes vs. no enteral intolerance outcome, using lagged covariates.
  • Strongly suggest using REDCap for data collection - easier to build databases, especially with assistance from REDCap clinics, and built to be easy for statisticians to export data on the back end.
  • Secondary outcomes (ICU/hospital LOS, etc) are mostly descriptive.
  • For VICTR voucher, we believe this project will fit within the voucher time frame (90-100 hours).

Mike Benvenuti, medical student

  • I have been working with some retrospective data and am not sure how best to represent the effects of antibiotics on culture yield (odds ratio, negative/positive predictive valueÖ) and would like some quick input. I also have another data set and I would like to show that patients have a d-dimer above the clinical threshold following joint replacement and am again not sure how to best show that.
  • ~200 patients. Primary relationship of interest: time of first antibiotic use vs. hospital LOS and/or possible secondary outcome: extension of antibiotic prescription post-hospital stay
  • Issues: immortal time bias - patients who get antibiotics later are, by necessity, in the hospital longer. Could use time-varying Cox model to address this? Looking at secondary, post-hospital outcome would help with this issue but could be a more blunt measure, and may not be that helpful depending on how many patients had antibiotic prescriptions extended.
  • Analysis will be complicated either way - suggest talking to MarioDavidson and/or research immersion course instructors about how to get stats support.
  • Definitely need to adjust for confounders (severity of infection, etc).
 

2015 September 23

Don Arnold, Pediatric Emergency Medicine

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  • Dr. Arnold is applying for VICTR biostatistics support for writing the analysis plan and sample size justification for his grant proposal. I estimate that this will take approximately 20 hours of support.
  • This is a cluster-randomized trial and will have correlation due to providers on multiple patients, so we recommend an analysis that will account for that, such as GEE, mixed effects models.
 
  • "We propose a 2-arm cluster (by clinician) RCT of the Asthma Prediction Rule (APR) in 3 children's hospitals to determine if implementation of the APR electronically will result in a decrease of the "unnecessary hospitalization rate" for children with acute asthma exacerbations from 23% to about 19%. My specific question is around the power calculations I'm doing. For an effect size this small I'm calculating that I need a couple thousand clinicians in each arm, whereas we will likely have at most 60 in each arm. I'm looking to alternative designs or methods to analyze the data."
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Richard Lesperance, Surgery

  • Want to use an ICC to measure interobserver reliability. We recommend giving a confidence interval along with the point estimate.
  • A useful measure for one of the main analyses could be the mean difference (with confidence interval) in length between the anterior and lateral chest wall.
  • "1) For measurements of chest wall thickness among trauma patients, should the mean measurements have standard deviation or 95% CI calculated for them? 2) We have 4 observers performing measurements on a total of 450 CT scans. How do we calculate / report inter-observer reliability? (Cohensís kappa?) Should all 4 observers rate the same 5 or 10 scans to obtain the measure?"
  • "Detailed background: We are reviewing about 450 CT scans of patients who had a pre-hospital intervention: needle decompression of a tension pneumothorax. This is a traumatic condition where pressure builds up inside the chest (but outside of the lungs), and can kill a patient unless the pressure is drained. Commonly, paramedics are taught to insert a needle through the chest wall to drain the pressure Ė but there are concerns that the needles may not be long enough. Previously published literature suggests that this procedure may be ineffective in many people due to chest wall thickness Ė there are several published studies using cadavers, and CT scans of healthy volunteers, that show quite often the chest wall is thicker than the needle size commonly used Ė if the needle canít reach the pleural space, the excess pressure cannot be drained.

We are looking at those 450 CT scans and measuring chest wall thickness in 2 locations Ė the front (where the procedure is traditionally taught, just below the clavicle) and also the side (which is an alternate location that some EMS providers are taught). We are measuring both sides (4 measurements total per CT scan).

Additionally, we are trying to measure the distance from the chest wall to the nearest cardiac structure (if this is too close, maybe the needle can injure something if we just use a longer needle!)

Besides the measurements (which other people have done, just not on actual trauma patients), we also theorize that many of these procedures are being performed unnecessarily. This condition is difficult to study because it is difficult to diagnose pre-hospital, and can be lethal if not effectively treated. The only marker that a patient both needed the needle decompression, and that is was successful, is the subjective EMS report that there was a ďwhooshĒ of air when inserting the needle and the patient ďgot betterĒ.

Further, we have noticed many patients who received a needle pre-hospital, but when they get to the CT scanner, they have no pneumothorax (air in the pleural space at all) Ė which means by definition, not only did they not need the procedure at all, but the procedure was completely ineffective. So we will be left with a % of patients who we can prove never had a pneumothorax and even though they had needle decompression attempted, it definitely failed.

We realize that this number is the minimum; in effect these are the patients we can PROVE had both inappropriate and ineffective treatment. The true number is much higher but unknowable.

Statistical questions: 1) Some previous literature reports the mean thickness and standard deviation Ė others report the mean and a 95% Confidence Interval. Is there a benefit to one way or another? 2) We have 4 people reviewing CT scans. What test should be used to measure inter-observer reliability? Should all 4 observers rate the same scans to obtain the measure? How many samples Ė 5, 10, more?"

2015 September 16

Jamie Robinson, Biomedical Informatics NLM Fellow

  • "My topic is Surgical Resection for CPAMs. In particular, I believe I need help with regression analysis."
  • We discussed specifics of R code, how to fit splines to a continuous variable and how to report the resulting estimates.

Thomas An, Medical student

  • "I am trying to compare hospital outcomes for MRSA vs. MSSA pediatric infection. I have done some analysis in GraphPad that I am not sure is correct."
  • We discussed non-parametric tests to use (Wilcoxon Rank Sum test, Kruskal-Wallis) as opposed to parametric tests. We also discussed whether a regression that adjusts for potential confounders is appropriate.
 

2015 September 9

Isa Wismann-Horther and Katie Ryan, OB/GYN

  • The project is a retrospective case control study looking at healthcare system based factors (postpartum counseling on birth control etc.) and their affect on short interval pregnancies. We wanted to meet with you all before we finished our IRB to make sure our methods were using the best possible statistical model of analysis. We were thinking about creating a scale to make a composite score which would put a numerical value to the descriptive data we're looking at (if birth control counseling was documented in the chart before discharge, did they pick a preferred method etc.) We were curious how many charts we would need to look through to power the study.
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2015 September 30

Kelly Maguigan, Critical Care Pharmacy

  • Project involving enteral intolerance in spinal injury patients who are on concurrent vasopressors.

2015 September 23

Don Arnold, Pediatric Emergency Medicine

  • "We propose a 2-arm cluster (by clinician) RCT of the Asthma Prediction Rule (APR) in 3 children's hospitals to determine if implementation of the APR electronically will result in a decrease of the "unnecessary hospitalization rate" for children with acute asthma exacerbations from 23% to about 19%. My specific question is around the power calculations I'm doing. For an effect size this small I'm calculating that I need a couple thousand clinicians in each arm, whereas we will likely have at most 60 in each arm. I'm looking to alternative designs or methods to analyze the data."

2015 September 9

Isa Wismann-Horther and Katie Ryan, OB/GYN

  • The project is a retrospective case control study looking at healthcare system based factors (postpartum counseling on birth control etc.) and their affect on short interval pregnancies. We wanted to meet with you all before we finished our IRB to make sure our methods were using the best possible statistical model of analysis. We were thinking about creating a scale to make a composite score which would put a numerical value to the descriptive data we're looking at (if birth control counseling was documented in the chart before discharge, did they pick a preferred method etc.) We were curious how many charts we would need to look through to power the study.
  • We recommended that the group write out their specific aims in order to keep them as focused as possible.
  • We also recommended that they check out whether a VICTR design studio would be appropriate for more intense statistical support. We also recommended that they check if there is a collaboration between our department and OB-GYN.
  • We discussed what would be an appropriate outcome. We advised against using a scoring system. Rather, we suggested they consider either looking at the elements of counseling and treat it as an ordinal outcome (number of elements covered in counseling) or as a binary outcome (any counseling versus none).

2015 August 26

Carissa Cascio, Psychiatry

  • "If possible, we'd like to review a recent VICTR submission and the pre-review feedback we received regarding our power analyses, assessment of normality, and imputation."
  • Addressed three critiques from VICTR application:
  • Power calculations: initially used difference seen in pilot data; suggest adding power analyses using the smallest effect size that would be clinically meaningful.
  • Assessments of normality (for t-tests): Suggest using nonparametric versions (Wilcoxon) instead of assessing normality. Loss of power is minimal if assumptions are met, and benefit is large if assumptions are not met.
  • Multiple imputation: Attrition is expected to be a major issue, but not sure if data will be missing at random (an assumption of multiple imputation). Suggested doing complete case analyses as primary, and doing multiply imputed analyses as a secondary analysis. Specify beforehand what variables will be used for imputation, and describe the differences between patients with and without missing data.
  • Possibly helpful paper for types of missing data for missing imputation: http://www.sciencedirect.com/science/article/pii/S0895435606001971

2015 August 19

Maya Yiadom, Emergency Medicine

  • Looking at differences in screening for EKG vs. stemi incidence for multiple emergency departments for pilot data for a grant submission. Each center has its own screening criteria, and rates of stemis vary across centers.
  • Suggested simple descriptive analysis: table with one row per center describing incidence, sensitivity, specificity, NPV and PPV with confidence intervals. Think about describing demographics as well.

Jamie Robinson, General Surgery

  • Wants to look at the difference in costs using PHIS on performing appendectomy + 30 days post op before and after the intervention of a clinical practice guideline.
  • Didn't come to clinic.

Brian Long, Surgery

  • Looking at risk of cancer recurrence in pediatric neuroblastoma patients and whether surgery is helpful in high-risk patients. At the very beginning of the process (database design, etc for a retrospective multicenter study), but looking ahead to requesting a VICTR voucher for statistical analysis. Analysis will likely involve multiple survival models and competing risks, so we recommend the 90-hour VICTR voucher level to accommodate all analyses and data management. Also suggested attending the REDCap clinic for help with database design, and gave some data collection suggestions.

2015 August 12

Brian Long, Surgery

  • Planning a pilot retrospective chart review
  • Brought a proposed variable list for data collection and proposed outcome measures for a retrospective study looking at outcomes after an operation.
  • Research questions of interest are: Which patients with high-risk disease will benefit from surgical resection of the primary tumor? Does complete resection of the primary tumor benefit patients with high-risk disease? Do patients in whom a complete resection is unlikely to be accomplished benefit from partial resection of their tumor?
  • Discussed data needed for survival analysis: cause of death, last date of follow up, etc.
  • Discussed types of survival events
  • We discussed applying for VICTR biostatistics support, but we did not estimate the time required yet.

2015 Aug 5

Nick Carter, Surgery resident

  • The sample size estimation is completed using the Kappa statistic. With a total measurements of 48, it provides at least 80% power to detect a Kappa = 0.8 with a two-sided type I error = 5%.

2015 July 29

Nick Carter, Surgery resident

  • I am working on a study trying to show non-inferiority of postoperative care provided by community health workers compared to standard postop care with the operating surgeon. We are just getting started in planning the study, and I hoped to discuss study design and power with a statistician."
  • Study will take place in Haiti. Prevalence of wound infection is very low (~5%), so getting enough patients will be difficult. Discussed a validity study, showing sensitivity/specificity/PPV/NPV with confidence intervals, using surgeon in-person assessment as gold standard and both a) surgeon via cell phone picture and b) community health worker assessment as new diagnostic tools. Due to low prevalence, will need a lot of patients to get a "reasonable" (according to clinical judgment) confidence interval for each quantity.
  • Discussed adding "dirty" infection sites as well to raise prevalence to an estimated 10-20% - something to think about.
  • Link to PS sample size software (Windows)
  • Link to VICTR studio page

2015 July 22

Tracy Marien, Endourology and laparoscopic surgery

  • "I am from the Urologic Surgery department. Basically, my primary question is as follows: I was performing a multi-regression analysis in Stata to assess which factors are associated with passage of ureteral stones. However, it appears that while two factors are significant they also cancel each other out because they are so strongly associated. Is there a way to control for this?"
  • She has ~ 100 patients with kidney stones, some of whom pass the stone without intervention and some that need surgical intervention. Standard of care is to measure the size of the stone in the axial direction with CT scan. Coronal measurements are also made but are not referenced typically. Her hypothesis was to investigate whether both measurements would help predict who requires surgical intervention.
  • We discussed looking at the scatter plot of the axial and coronal measurements and found there to be a strong linear relationship. We advised against fitting both variables in the model of interest. We also advised to pre-specify the model based on literature and clinical knowledge rather than univariate tests. We also discussed the 10:1 or 20:1 ratio for determining the complexity of the model based on the minimum of events/non-events.
  • Because of the strong correlation observed in the scatter plot, we discussed including an aspect ratio variable in the model with either axial or coronal measurement to assess whether this would be a question of interest.

2015 July 15

Jonathan Siktberg and Mayur Patel, TBI

  • Project on diffuse axonal injury; sent PPT slides
  • Team has a good list of aims, models and covariates: POLR model for GOSE score, linear regression for quality of life score - these seem appropriate provided assumptions are met with data. Possibly a Cox proportional hazards model for mortality, if this outcome is of clinical interest.
  • Discussed multiple imputation, since many patients could not be reached for followup (approximately 35%). Recommended doing both multiply imputed and complete case analyses to compare results; multiply imputed analyses may be less biased. Clinical data other than model covariates can be incorporated into imputation.
  • Currently patients are classified as DAI negative or positive, with positive having three possible grades. We recommended doing models with a four-level variable for DAI (0/1/2/3); to address the more typical clinical question of any vs. no shear (DAI positive vs. negative), could redo the model dichotomizing into positive vs. negative.
  • The group plans to apply for a VICTR voucher. Given the possible complexities of multiple models and multiple imputation, we estimate 90 hours.

Jamie Kuck, Division of Allergy, Pulmonary, and Critical Care Medicine

  • "Sepsis patients have high levels of cell-free hemoglobin in their plasma, and these levels are associated with increased risk of mortality. While exploring the possible mechanism of cell-free hemoglobin, I measured levels of oxidized LDL in sepsis patient plasma and found that those patients with high cell-free hemoglobin have low levels of oxLDL, which was a surprise. An endocrinologist suggested that we then look at LDL levels since sepsis patients usually have low amounts, which could explain the low amounts of oxLDL."
  • We suggested a linear regression model like this: oxidized LDL = hemoglobin + total LDL. Prism doesn't seem to be capable of this, so get SPSS and look at examples on UCLA stats web site for instructions.

2015 July 8

Nick Kramer, M3 Meharry Medical College

  • He would like continued input on his project.
  • We discussed how clinical judgment should guide the selection of manuscripts to include in their systematic review. We also discussed how to organize their analysis from the big picture of outcomes by type of fracture (simple versus compound) or type of repair (screws versus plates).

Christopher Brown, Dept Internal Medicine

  • The project is fairly simple, we measure labs once a day or twice a day to follow potassium. I would like the primary end point to be regarding this lab, meaning --if you measure the potassium twice a day, does the potassium stay in the normal range for more time during the patients hospitalization than if you measure it only once a day--. However because I am measuring the value more often in one group than the other I am wondering what the method for accounting for this statistically would be, as there appears to be a sampling bias between the groups. It was suggested to me that this could be accounted for with a "generalized least squares approach" however I do not completely understand how that regression adjustment would help me. In any case, can you tell me if it is possible to compare the time (persondays or personhours) a variable spends between two values (IE potassium between 3.5 and 5.0) when the two groups involved are sampling the variable with different frequencies? (IE I can detect twice as much low or high values in theory twice a day than once a day, so how do I compare these groups)
  • We discussed how to approach analysis of the prospective data -- create an indicator for whether the subsequent days lab was within normal range or not treating the 1x/day or 2x/day as a treatment group variable (standard of care versus intervention) and how this analysis would require a repeated measures analysis such as GEE. As secondary descriptives, we discussed calculating the proportion of measurements outside of normal range in each of the groups.
  • We estimate that this analysis will require 40 hours of a VICTR statisitican's time.
  • We also discussed the retrospective data abstracted from the medical record and methods of analyzing it. This also will require some type of repeated measures analysis as well as decisions on how to handle repeat hospitalizations per subject. We estimate that the retrospective analysis would require 60 hours of a VICTR statistician's time.
 

2015 July 1

Kendra Parekh, Assistant Professor Department of Emergency Medicine

  • "I would like to attend a biostat clinic on July 1 to discuss data analysis for a survey that evaluated emergency medical techniciansí, nursesí, and physiciansí attitudes toward a new Emergency Medical Services system in Georgetown, Guyana."
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  • She has responses from 17 EMTs for one survey and about half MDs and half RNs who filled out the second survey. There were about 10 questions between the surveys that were the same. We recommended using the chi-square test with continuity correction to assess whether there was an association between provider type and responses to the question.
  • We recommended she use graphs to display the data as well as tables with proportions rather than simply relying on p-values from tests of association.
 

Alexander Gelbard, Otolaryngology

  • I am looking at disease of unexplained scarring in the trachea. We looked at the biology of the fibrotic tissue response, and then investigated the association with defined respiratory bacteria. Finally we investigated activation of immunologic pathways in the tracheal tissue samples.

    Expt 1. qPCR results (10 experimental, vs 3 controls)- *the controls are 23 pooled donors preformed in triplicate.

    Expt 2. PCR results (binary yes/no presence of detectable bacterial) in 10 experimental vs 10 controls.

    Expt 3. In isitu hybridiation (binary assessments of staining positive/negative) with 10 experimental vs 10 controls, and 10 normals

    Expt 4. Comparision of % positive cells in Transmission Electron microscopy immunogold staining.

    Expt 5. Elispot comparison. IFNgamma release in response to antigen specific stimulation. 10 experimental, 10 controls.

    Expt 6. Comparison of immunohistochemistry quantification. 10 experimental vs 10 controls and 10 normals.

    Expt 7. qPCR results (10 experimental, vs 3 controls) - *the controls are 23 pooled donors preformed in triplicate.

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  • We recommend that he contact the Friday clinic for a question involving the replication of pooled data for the healthy normal group. Otherwise, we recommended non-parametric tests and chi-square with continuity correction, as appropriate.

Daniel Heath Hagaman, Anesthesiology

  • We advised that he get a copy of SPSS rather than using Excel.
  • We suggest chi-square for difference in proportions of forms filled out in VPEC before and after intervention. Include a few months of wash out period after to get a more stable estimate. To look at factors to predict forms being filled out among non-VPEC providers, use logistic regression and include covariates based on clinical knowledge. Ideally, a random effect for provider should be included.
 

2015 June 24

Laura Wilson, Hearing & Speech Pathology

  • "I am seeking expertise regarding the data analysis plan for a study on school outcomes after sports-related concussion."
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The Biostatistics Clinic on Wednesdays is dedicated to biostatistics applications in surgery, anesthesiology, and emergency and critical care medicine.
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2015 July 1

Kendra Parekh, Assistant Professor Department of Emergency Medicine

  • "I would like to attend a biostat clinic on July 1 to discuss data analysis for a survey that evaluated emergency medical techniciansí, nursesí, and physiciansí attitudes toward a new Emergency Medical Services system in Georgetown, Guyana."

Alexander Gelbard, Otolaryngology

  • I am looking at disease of unexplained scarring in the trachea. We looked at the biology of the fibrotic tissue response, and then investigated the association with defined respiratory bacteria. Finally we investigated activation of immunologic pathways in the tracheal tissue samples.

    Expt 1. qPCR results (10 experimental, vs 3 controls)- *the controls are 23 pooled donors preformed in triplicate.

    Expt 2. PCR results (binary yes/no presence of detectable bacterial) in 10 experimental vs 10 controls.

    Expt 3. In isitu hybridiation (binary assessments of staining positive/negative) with 10 experimental vs 10 controls, and 10 normals

    Expt 4. Comparision of % positive cells in Transmission Electron microscopy immunogold staining.

    Expt 5. Elispot comparison. IFNgamma release in response to antigen specific stimulation. 10 experimental, 10 controls.

    Expt 6. Comparison of immunohistochemistry quantification. 10 experimental vs 10 controls and 10 normals.

    Expt 7. qPCR results (10 experimental, vs 3 controls) - *the controls are 23 pooled donors preformed in triplicate.

2015 June 24

Laura Wilson, Hearing & Speech Pathology

  • "I am seeking expertise regarding the data analysis plan for a study on school outcomes after sports-related concussion."
  • Survey study of ~120 patients (age 13-17) who were seen for concussions during the last school year, following up on academic performance, special accommodations (extra test time, sitting out gym), and satisfaction with return to school. Hypothesis is that school absences and accommodations are a) correlated and b) both affect academic performance and satisfaction, which are measured on both parents and children.
  • For future studies, mediation or PATH analyses might be appropriate, but these models will need to be simple due to data and sample sizes. Suggest proportional odds or logistic regression depending on distribution of final outcomes (have multiple levels, but if some levels are not well represented, may make sense to combine). Base effective degrees of freedom on final outcome levels Ns - for logistic, the minimum of the two outcome categories. Do not use univariate analyses to prioritize covariates; rather, develop a priority ranking based on clinical knowledge and importance in hypotheses.

2015 June 17

Trisha Pasricha, 4th year medical student

  • "I am a 4th year medical student who has completed a study at the Vanderbilt Center for Surgical Weight Loss that looks at the correlation between depressive symptoms and BMI/medical co-morbidities in patients who have sought surgical and medical weight loss at the center. I would like some help determining if we have used the correct analysis of our data."
  • Info about the study: Complete data on 38 patients, but only 8 medically treated patients - lots of loss to followup in this group. Therefore, any inferences about treatment will need to be approached from the standpoint of very preliminary research. Due to limited sample size and complex research questions, we can't really put all exposures of interest in the same model.
  • Possible models:
  • post-treatment BDI = (% change BMI) * comorbidities + pre-treatment BDI; this answers the question of whether, among patients equally depressed at baseline, there is any association between % change in BMI and/or comorbidities and post-treatment depression, and whether the association for comorbidities changes based on % change BMI, and/or vice versa.
  • post-treatment BDI = (% change BMI) * medical vs. surgical treatment + pre-treatment BDI; this answers the question of whether, among patients equally depressed at baseline, there is any association between % change in BMI and/or treatment type and post-treatment depression, and whether the association for treatment changes based on % change BMI, and/or vice versa.
  • post-treatment BDI = treatment * comorbidities + pre-treatment BDI; this answers the question of whether, among patients equally depressed at baseline, there is any association between treatment type and/or comorbidities and post-treatment depression, and whether the association for comorbidities changes based on treatment type and/or vice versa.
  • BDI (Beck Depression Inventory) is typically very skewed. Suggested checking histograms but probably using an ordinal logistic regression model (also called proportional odds logistic regression). Trisha is currently using Prism; if Prism can't handle POLR, check into SPSS (helpful link for SPSS: http://www.ats.ucla.edu/stat/spss/dae/ologit.htm)

2015 June 10

Kristy Kummerow, General surgery resident

  • "I am a surgical resident and would like to attend a Biostats clinic to request help doing multiple imputation in Stata. I would prefer tomorrow (Wednesday) if there is still space, or Thursday. Please let me know whether either of these are options."
  • We discussed what should go in the imputation model (outcome to be included) and found links to help with the syntax for fitting the MI model and the final model with the MI results.

Michael Kenes, PGY-2 Critical Care Pharmacy Resident

  • "My study is looking at clinical outcomes of Clostridium difficile infections in neutropenic patients. I have collected all of the data and performed univariate analysis. I am in need of assistance in discussing how to handle patients who died within the study timeframe as well as a regression analysis."
  • Since time to diarrhea resolution was captured in the data, we recommended they use a competing risk analysis to handle the deaths.
  • Due to the low number of events, we discussed pre-specifying the model as opposed to using univariate analyses to drive model selection as well as propensity scores for data reduction.

2015 June 3

Jennifer Hale, Pediatric Pharmacy

  • "Evaluation of a computerized prescriber order entry protocol for pain management and sedation in a pediatric cardiac intensive care unit"
  • We discussed alternate outcomes besides total med dosage, ventilator-free days (necessary to have a common denominator to alleviate bias in differing stays in the ICU).
  • We recommended the Wilcoxon Rank Sum test rather than the t-test for the unadjusted tests with continuous outcomes. We also discussed fitting models to adjust for potential confounding. If normality assumptions are met, then the linear model would be most appropriate; if they are not, then other types of models need to be considered such as the logistic regression or proportional odds model.
  • We also suggested investigating average dose/day as an outcome.

Justin Bachmann, Cardiovascular Medicine

  • Iíd like to attend the health services research biostatistics clinic today in D2221 MCN. Iím conducting an analysis of the association between self-efficacy and physical activity in a cohort of 2000 patients in the Vanderbilt Coronary Heart Disease Study. Physical activity is characterized as both a continuous (MET-minutes/week) and an ordinal (low, moderate, high) variable. The independent variables include continuous, categorical and ordinal variables. Iím using negative binomial regression as well as ordinal logistic regression and would like to get the statisticiansí thoughts on these models.
  • We suggested investigating whether SAS can run a zero-inflated negative binomial model or not and whether this is necessary given his data. We also discussed the robustness of the proportional odds model when the assumptions are borderline, especially when using the Score test p-value to make the determination.

2015 May 20

Tim Shaver, Biochemistry

  • Per the email: "I am a Biochemistry graduate student working to develop a retrospective study of the correlation of novel gene fusion events with biochemical recurrence following radical prostatectomy. I previously attended the Thursday clinic on May 7 and received some guidance regarding sample size justification for an upcoming VICTR proposal. However, I have encountered some difficulties implementing your advice due to incomplete reporting in our test data set. I would like to bring some of the specific numbers and receive feedback on a new plan for our sample size and power calculations."
  • This study is a case-control study. The main issue is that the pilot data may be inaccurately or incompletely coded, so pilot estimates might be incorrect. We recommended 1) removing patients with no information from the test data set (a sizable number) in order to avoid artificially inflating the denominator; 2) using PS's case-control functionality to calculate the difference in proportions they can detect with the 300 patients they plan to sequence at various levels of recurrence rate among the general population.

2015 May 13

Dikshya Bastakoty, Department of Pathology, Microbiology, and Immunology

  • Per the email: "I am applying for a VICTR grant (part of which involves analysis of human samples for gene expression), and was looking for help with sample size determination based on recommendation by the biostatician who reviewed my grant."
  • We recommended that she check what detectable alternatives she could detect for each of her 3 primary proteins of interest given the maximum number of subjects she could afford.
  • We also recommended that she review the literature to see if other studies existed that supported the difference she was using for her current power calculation.
  • If the current number she reports is all that can be feasibly recruited and afforded in the time and budget constraints, we recommended she highlight that in the application.

2015 April 29

Jim Jackson and Jo Ellen Wilson, VA Quality Scholar Program

  • Per her email: "I am needing a quote for the amount of time it would require to develop an analysis plan and perform analysis for a proposed project of mine. I will be using this information to submit a request for funding from VICTR."
  • Recommended keeping it to aims 1-4, since we're not sure we have adequate data to answer aim 5. Estimate 90 hours for two manuscripts (data set is very clean, and VICTR statistician is familiar with it). We edited Jo Ellen's aims to reflect discussion of modeling specifics in clinic - Jo Ellen has this document.
 

2015 April 15

Eric Wise, Department of Surgery

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  • Spearman correlation is univariate; multivariable regression adjusts for multiple potential confounders
  • Do NOT use univariate selection to decide which variables to put in models; instead use clinical judgment, literature search, hypothesis to decide which variables to put in - less subject to noise and confounding
  • Model selection (forward, backward, stepwise) is fraught with problems; using above approach -> no model selection algorithm, less likelihood of "data fishing"
  • Kaplan-Meier is univariate; Cox proportional hazards model good for time to event analyses with adjustment
 

2015 March 18

Alexandra Fish, Center for Human Genetics

  • I have a question regarding approaches to handling sampling zeros. I had previously conducted an analysis in which I used a likelihood ratio test
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  * The study uses 4 skulls with different targets per eye. In each skull, one eye is operated on using standard image-guided methods; the other eye uses the enhancement to the standard methods. Sixteen surgeons were tested, each operating on each of the skulls. The order of skulls for each surgeon was the same but the order of methods of surgery was randomized. * Currently, he has tested for differences using t-tests and F-tests. * Our recommendation was to use linear and logistic mixed effects models to account for the correlation among surgeon, including method of surgery, skull, and eye (?) as covariates in the model with surgeon as the random effect.
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2013 December 11

Catherine Bulka, Anesthesiology

  • Wants to demonstrate/examine group balance wrt baseline characteristics after matching using propensity scores.
  • Calculated propensity scores for receiving regional anesthesia during surgery, as opposed to general anesthesia, based on several factors that anesthesiologists in the department deemed important in their decision making process, such as the age of the patient, duration of the surgical procedure, etc. I then matched patients who received regional anesthesia to those who received general anesthesia by their propensity score.
  • consider calculating the "standardized differences" between the groups and plotting it. Something like this: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472075/figure/fig01/
  • Computing confidence limits for effects are probably in order, as opposed to hypothesis testing. It's not just large samples where hypothesis testing presents interpretation
problems; we generally like interval estimation.
  • In building the propensity score it is appropriate to not be parsimonious (i.e., it is not approriate to remove variables from the model), and to allow effects of continuous variables to be nonlinear.
  • There is controversy over matching vs. covariate adjustment for logit propensity score. Matching usually results in some patients being discarded, which is problematic.
  • research question is to compare hospital length of stay in those who got regional anesthesia vs. regional. They used prop. score matching to match.
  • Wants to demonstrate group balance wrt several variables.
  • Instead of using propensity scores to select the patients, you could take all patients who met inclusion criteria, but use weighted propensity score analysis
  • Catherine did try the standardized differences that Meng suggested, but the problem is the surgical duration variable still looks like it is too different. However, it turns out that the standard deviations she used were from the selected patients, while the paper recommends using the ones from the original group of patients from which the two groups were selected. She will re-evaluate the quantities and reassess whether the groups are balanced.
  • If the groups still don't look like they are comparable, could look at some of the methods Robert Greevy has written for matching in observational studies.

2013 December 4

Jo Ellen Wilson, Psychiatry fellow in psychosomatic medicine

  • Needs AP and quote for VICTR proposal.
  • Not a reliable way of distinguishing delirium from catatonia; plans to assess patients in two ongoing delirium studies for catatonia to see if there is a clinically relevant overlap between definitions
  • Main study personnel will administer main delirium assessment (CAM-ICU - yes, no or unable to assess); Jo Ellen will administer, within 2 hours, delirium subtype screening (hyperactive, hypoactive, mixed or no delirium) and catatonia screening (scoring: first 14 items are administered; if patient meets two criteria, considered to have catatonia and an additional 9 items are done to assess severity)
  • Delirium subtype screening and catatonia screening both look at previous 24 hours, whereas CAM-ICU is immediate, current assessment
  • Technically, patients can't be considered catatonic if delirious
  • Suggest doing entire Busch-Francis assessment even on patients who don't meet catatonia criteria on items 1-14
  • Plan to look at individual assessments (two per day in ICU, one per day in wards), not full days or summary measures
  • Treatments for delirium and catatonia are sometimes completely opposite - e.g., benzodiazepines avoided in delirious patients but recommended for catatonia, and vice versa for antipsychotics
  • Aims 1-2 focusing on in-hospital outcomes (agreement between delirium and catatonia assessments); aim 3 focuses on hospital LOS, long-term outcomes, and might require more complex modeling to account for possible relationship between delirium/catatonia diagnosis and treatment
  • Aims 4 & 5 deal with medications - parent databases collect 24h totals, but Jo Ellen could get exact times and doses from StarPanel later
  • Suggest splitting into two projects: aims 1 & 2 (perhaps 4 & 5, depending on what exactly is needed - Jo Ellen will discuss with mentor Wes Ely), then aim 3 - long-term and clinical outcomes
  • For sample size calculations, will need to think about specific differences hoped for (differences in proportion or test scores, for example); Jennifer will look at parent study enrollment numbers for average # enrolled per month at VUMC
  • For aims 1 & 2, we estimate 50 hours (likely using kappa statistics for delirium vs catatonia agreement and model of catatonia score ~ CAM [yes/no/UTA] with repeated measures for delirium vs catatonia scores); if aims 4 & 5 are added, more time will be needed. Aim 3 will be addressed at a later date.

2013 November 20

Justin Gregg, Chang

  • previously approved study with VICTR funding that may need some additional statistical support ($1100 left from VICTR grant)
  • looking at time to recurrence/progression of non-muscular invasive bladder disease
  • retrospective data collected 2002-2011
  • could possibly fit under collaboration with anesthesiology - suggest talking to anesthesiology department first
  • suggest estimate of 60 hours to account for data management and analysis if VICTR grant is needed

Mayur Patel

  • Prospero-082713.pdf: Mayur's file
  • systematic review. Want to know whether any meta-analytical technique is possible considering it is largely based on pre-post retrospective case-series data (mostly exposure only, partial cohort type studies; full 2x2 rarely available) and where the event rate is nearly always zero.
  • Our current thought is that there is no analysis possible here Ė but wanted to confirm that, as I've read about continuity correction of 0.5 for zero-event studies, but that seems more applicable for non-repeated measure type samples where controls/treatment groups are distinct.
  • http://www.prisma-statement.org/statement.htm
  • He provided more detail on the studies included in the systematic review. Our recommendation was that a meta-analysis would not be appropriate but directed him to prisma-statement.org

2013 October 30

Jackie Shuplock, Pediatric Cardiology

  • Prospective cohort (2007-2013) of cardiac surgery patients. Some received dex, others did not. Interested in examining the association of dex use with cardiac arrhythmias in the post-op period.
  • Needed input on how best to define the model of interest. Currently using a variable selection process based on univariate analyses. Concerned about differing significance with the inclusion/exclusion of particular covariates. Recommended using clinical knowledge and information from literature to guide the selection of covariates.
  • Four anesthesiologists were involved in the surgeries (any need to adjust for anesthesiologist?) and there were a total of 783 arrhythmias recorded.
  • Dex was rarely used 2007-2008. We recommended limiting the scope of data to 2008 - 2013 to avoid any kind of time bias. We also recommended adjusting for year of surgery in the model in case there were any changes in surgical protocol across time.

2013 Rocktober 21

Francheska Desravines, Meharry

From last clinic:
  • study: The risk of early discharge following pediatric cardiac catheterizations in infants and young children.
  • aim is to find relative risk of a minor or major complication occurring after 6hrs post cardiac catheterization procedure.
  • .xlsx file saved as csv on ClinicsData
  • Data are retrospective chart review on kids 0-4 with cardiac cath between July 2007 and 2012
  • Want estimate of proportion of events with confidence interval
  • To identify important factors, can fit a multivariable model with factors such as age, diagnosis (one v. two ventricle), source of pulmonary
blood flow
  • Data on children over 4 would be important.
  • There were 24 patients with major complications that would require major intervention
  • For a 15 kg child, the estimated model-based prob of major complication is 0.026 (95% CI 0.015-0.046). logistic regression with weight
  • Can see Vincent Agboto, who is a statistician at Meharry.
A new dataset is on ClinicsData.
  • Describe the outcome (major complications) against some of the variables graphically, as in boxplots
<> > binconf(x = sum(card$majorYN == "Major complication"), n = nrow(card),
+ method = "wilson") PointEst Lower Upper 0.03259452 0.02217373 0.04767391
  • The (unadjusted) estimated probability of a major complication is 0.03 (95% CI 0.02, 0.05).
  • Recommend a forest plot showing the model-based probability of major complications with CIs for each combination of ventricles and blood source.

Catherine Bulka, Anesthesiology

Questions:
  • Looking to identify risk factors for developing pnemonia.
  • In her poisson regression, she used the binary outcome of whether the patient developed pnemonia
  • Discussed best way to measure outcome.
  • Have been using a Poisson regression model with a log link function and person-time offset. The person-time was calculated as the time the patient was at risk for developing postop pneumonia (i.e. #days between date of surgery and pneumonia onset, death, or hospital discharge). I am now questioning whether another type of model would have been more appropriate. I was trying to avoid survival analyses because in this context, it seemed like rate ratios made more sense as opposed to hazard ratios. I also wanted to account for time at risk in the model, since some patients were lost to follow-up, which is why I avoided logistic regression. Is Poisson regression the appropriate choice for my data?
  • Had been using a backwards elimination approach to select the final model, but there are a lot of issues with using statistical significance to identify which variables to keep in the model. Is there a better method? I have about 20 covariates that have been identified as potential confounders/effect modifiers.
  • A reviewer suggested using bootstrapping for model selection. While I am able to create bootstrapped samples with 1,000 replications and replacement in SAS, Iím not sure how to use these samples for variable selection.
  • We discussed data reduction techniques as an alternative to backward elimination. You could use a propensity score.
  • Cluster analysis.

2013 September 25

Heather Jackson, Anesthesiology, and Kyla Stripling, Neurosurgery

  • Planning small study (30 pts) examining a pain consult prior to surgery: "Does a preoperative pain management consult effect post-operative lumbar surgical outcomes?"
  • Research protocol attached
  • Contact Anesthesiology admin to see whether you have access to the existing collaboration with Department of Biostatistics (Jon Schildcrout and Matt Shottwell)
  • Recommend trying to get a mentor who has some research experience
  • One of the main outcomes is the ODI, a disability index.
  • May need to consider the sample size you would need to have power to show the differences you are interested in showing.
  • To calculate the required sample size, consider the smallest clinically meaningful difference in ODI and also the distribution of the outcome, including standard deviation.

Jun Dai, Epidemiology

  • Jun is responding to reviewer comments to her manuscript.
  • She has a survival model with covariates for the amount of food eaten. There are three types of fruit, and three corresponding variables.
  • The original research question was about the effect of total fruit consumption on survival. However, the reviewers asked for analysis which considered the amount of fruit separately by type of fruit. Then the reviewer wanted a test of whether the effects of the different fruits are the same.
  • We recommended that she respectfully explain that while his questions are interesting, they are beyond the scope of the paper.
  • Additionally, the test the reviewer recommended is a test for interaction, and doesn't address his own question. :[
  • We recommend that she give some descriptive summaries of graphs showing the types of fruits in her data to answer this reviewer's question without deviating from the paper's original focus.

Vaibhav Janve, Institute of Imaging Science

* Hakmook Kang works with imaging data and with Vanderbilt University Institute of Imaging Science, and we recommend he contact him.

2013 September 18

2013 September 11

Scott McLaurin, Rehab Services

  • Does physical/occupational therapy affect hospital length of stay?
  • Stroke patients
  • Patients were assigned a frequency of visits. The patients were assigned to be in the "meet frequency" group by a systematic algorithm. The patients who were in the "experimental" group were ensured to get their recommended therapy visits. The other group got standard of care.
  • Want direction for designing a new study
  • Could store the data in redcap
  • Could think about using a randomized list of group assignments. There is a mechanism for this in redcap.
  • Need to think about factors that would influence length of stay. You would need to collect these quantities and can adjust for these using a multivariable analysis.
  • We think their project might come under Dan Byrne and Hank Dominico's project.
  • Could go to redcap clinic to help ensure you set up the data the best way. Discussed data quality checks you can build into your database. See https://www.mc.vanderbilt.edu/gcm/rate/index.php/course/view/id/0168
  • If you are not covered under Dan's project, and funds are available, can use BCC (Biostatistics Collaboration). Contact Rameela.

Trisha Pasricha, medical student; mentor Clements.

  • study in GI Laproscopic Surgery
  • in the experimental design stage
  • looking at patient scores on the Beck Depression Inventory before bariatric surgery and scores 1 year following surgery to assess any correlation between bariatric surgery and depression.
  • The Beck Depression Inventory is a 21 question survey that results in a numerical score of 0-63, with higher scores indicating more severe depression and lower scores indicating minimal depression.
  • have several questions about the optimal experimental design (debating between 2 different designs in particular) and how to power the project accordingly.
  • Plan to readminister Beck one year after surgery.
  • Population who have pre-measurment is people who have bariatric surgery.
  • Discussed problems with inference based on pre-post designs.
  • Need to consider the best control population to make the inferences that are of interest. Could consider a general patient group that had any surgery or a specific type of surgery, or a group of obese patients that did not have bariatric surgery. Would want to ensure that patients who did not get the surgery are not systematically different from the patients who did have the surgery. Examples of this would include making the control group consist of the people who were ineligible for the surgery because they were unable to follow the pre-treatment regimen.
  • Discussed importance of minimizing bias that could be caused by people not coming to the one-year follow-up visit. This could bias the results towards patients who are already are not too depressed to come to the follow-up visit. Making the follow-up assessment web-based would mitigate this, but you would also want the pre- and post- surgery assessments to use the same modality.
  • Also discussed looking at the correlation between amount of weight lost and depression
  • Group wants to consider whether they should select patients to include in the study based on degree of pre-treatment measurement. The concern is that there might not be enough patients with lower depression. If you include all patients, then you would get the maximum possible number of patients with low depression, and there wouldn't be value in excluding patients with higher depression.
  • Discussed considering confounding factors like social support. These could be controlled for in a multivariable analysis. The degree of complexity of the relationship (and thus the model required) would inform the sample size required.
  • We would need estimates of the standard deviation of the BDI score and its distribution to calculate the number of patients required.

2013 August 28

Angela Joshi, resident, VA

  • observational study of patients who undergo coronary angiogram via the radial artery to determine factors that can predispose to radial artery thrombosis following angiography.
  • wants to calculate number of patients needed
  • Planning a prospective observational study
  • Have a list of factors that they want to identify the association with having thrombosis
  • Have a limited period of time over which to collect patients
  • Estimate around 1200 patients could be collected
  • Estimate about 2 percent of patients will have an event, for about 24 events
  • We advised that with this number of events, you will not have much power to learn much
  • Recommended that they rank the variables in order of their research priorities. We will calculate power based on their top priority variables.

2013 August 21

Francheska Desravines, med student at Meharry

  • study: The risk of early discharge following pediatric cardiac catheterizations in infants and young children.
  • aim is to find relative risk of a minor or major complication occurring after 6hrs post cardiac catheterization procedure.
  • .xlsx file saved as csv on ClinicsData
  • Data are retrospective chart review on kids 0-4 with cardiac cath between July 2007 and 2012
  • Want estimate of proportion of events with confidence interval
  • To identify important factors, can fit a multivariable model with factors such as age, diagnosis (one v. two ventricle), source of pulmonary blood flow
  • Data on children over 4 would be important.
  • There were 24 patients with major complications that would require major intervention
  • For a 15 kg child, the estimated model-based prob of major complication is 0.026 (95% CI 0.015-0.046). logistic regression with weight
  • Can see Vincent Agboto, who is a statistician at Meharry.

JoAnn Alvarez, for Center for Surgical Quality Outcomes Research

  • Outcome is workup quality:
      None Incomplete   Complete 
      6002       1991       1248 
  • Plan on using prop odds
  • In this case, I think we can assume exchangeable cov structure.
  • County info: 1867 unique counties in the data, with 9241 patient records. Here is the distribution of number of observations for each county:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  40  43  44  45  46  47  48  49  53  55  58  59  60  64  71 102 139 161 
671 368 212 149  73  59  49  36  30  24  18  15  15  11  15  10  12   1   9   8   5   5   4   8   1   3   4   1   3   3   5   6   3   1   1   2   1   3   1   1   1   1   1   3   2   3   1   1   1   1   1   1   1   1   1   1 

So there were 671 counties that had only one record in the data, 368 that had 2 obs, and the county that had the most representation in the data had 161 observations.
  • Could use GEE or a random intercept for county to account for practice variation by location.
  • Let m be the number of observations per cluster (subject is the cluster in many longitudinal studies), and n is the number of clusters (subjects). So m is the number of obs per county, and n is the number of counties.
  • GEE
    • Efficiency issues
      • Choice of weight mat: In this case, I think we can assume the data have an exchangeable cov structure. Loss of efficiency should be very very small (Liang and Zeger 1986) with use of independence working covar when truth is exchangeable. Or we could use exchangeable weight mat.
      • correlation of covariates within subjects: when the covars are between-subject (do not vary within a person (county here)), negligible efficiency loss. Actually, would need to check this.
      • cluster sizes: we have small m (number in each cluster), which should mean small loss of efficiency (see slide 47 of mod 4)
    • If we want to do gee with non-independence weighting, could use geeglm or geese (see slide 87 of mod 4). Can use rob cov in Frank's rms package. This is GEE with independence weight matrix / working covariance.
    • Gee requires large sample, but we have that. (n>>m Number of different counties is much bigger than the number of obs per county)
    • GEE requires MCAR.
    • Disadvantage of using gee would be that you can't use LRT since you are not specifying the full likelihood. Can use wald tests.
    • Performance with sparse clusters? Is this separate from efficiency issues? In this case, we do have sparse clusters and many clusters of size 1. The issue is variability in the cluster sizes. Jon thinks this is a validity issue.
  • GLMM (generalized linear mixed effects model)
    • GLMM requires MAR.
    • Can implement via clmm in ordinal package. logit link gives prop odds.
    • With glmm, since it's a conditional model with a non-identity link, we cannot get population-level contrasts. The contrasts are subject-specific, and I believe they approximate the pop-level values.
    • The marginal parameters are smaller in magnitude than the conditional parameters. Can think of the conditional params as "inflated" marginal params?
    • There is actually a formula for the relationship between the conditional and marginal parameters in terms of the variance of the random effect (Zeger 1988).
    • Example of interpretation of subject-specific effects "exp β represents the ratio of the expected odds of respiratory infection for an individual with vitamin A deficiency to that same individual (with same covariate values but) replete with vitamin A"
  • Can check PO assumption using chi square lrt test (difference in deviance) between the prop odds model and the cumulative logits model.

2013 July 31, August 7 and 14

no clients

2013 July 17

David Osborn, Urology

  • We estimate that this would take about $3500 for a VICTR biostat support request
  • Retrospective study looking at urinary incontinence in patients with traumatic brain injury.
  • Want to estimate the incidence of incontinence in this group
  • Outcomes are death, hospital los, 3 outcomes

Breanna Michaels and Jennifer Morse

  • *Data attached below*
  • looking at patients who received non depolarizing neuromuscular blocks during surgery and their incidence of post operative residual curarization (PORC) (means leftover block) in the PACU.
  • Main outcome is train of four, and whether it is greater than 0.9, which is the designation for PORC. Train of four is a ratio of two weakess measures.
  • Recent studies have shown that patients with PORC have a higher risk of respiratory and non-respiratory post operative complications.
  • want to know the proportion of patients with PORC, as well as the factors that affect the occurrence of PORC.
  • Factors may include gender, type/dose of non-depolarizing block, or antibiotics given during surgery.
  • Discussed whether to use the train of four ratio or the dichotomization of PORC, which is greater than 0.9,
  • Want to see if there are systematic differences between those who had outcomes measured and those who did not.
  • Could fit a regression model to identify the important factors.
  • Recommend getting a confidence interval for the proportion with PORC
  • Try plotting a histogram of the outcome variable
  • Discussed events per variable. If you use a logistic regression, dichotomizing the outcome, use the number in the smaller group to determine the number of variables you can fit in your model. A rule of thumb is to have at least ten events per variable (parameter in model).

Roop Gill

  • We estimate that the first manuscript, which will encompass two outcomes DVT and seroma will require $4000.
  • Tummy tucks
  • Have a ten-year cohort of patients
  • Want to see if skin-only abdominoplasty
  • Want to calculate the power they have for their questions with the data they have.
  • One of the main outcomes of interest is deep vein thrombosis, which is rare, so you will need a very large number of patients

2013 July 10

Bill Wester, ID/VIGH

  • Bill first consulted the group on 6/19; he would like to continue discussion his proposed study "Long-Term Renal Outcomes Among HIV-1 Infected and cART-Treated Adults in South Africa" during biostatistics clinic.
  • Need an estimate of hours/cost for VICTR biostatistics support.
  • Last time we postponed the estimate to wait for more information on the condition of the data.
  • Now we have more information on the completeness of the data. Dr. Wester is going to look through the data dictionary and the data itself to assess how clean it is.
  • Dr. Wester has decided to concentrate on Aims 1-3.
  • We estimate that this will require 100 hours of support.
  • Additional data
    • 1. How many total patients in the cohort? 2500
    • 2. How many (what %) have baseline urinary protein dipstick results? 2000
    • 3. How many (what %) have longitudinal urinary protein dipstick results/measurements; and at what frequency (I suspect they are irregularly done; but can we see how many (what %) have 1 year post ART and ? 2 year post ART values recorded/captured? 75% have a second measure
    • 4. How many (what %) have baseline creatinine results? The majority.
    • 5. How many (what %) have longitudinal creatinine results/measurements; and at what frequency (I suspect they are irregularly done; but can we see how many (what %) have 1 year post ART and ? 2 year post ART values recorded/captured? 75%
    • 6. How many were ART-naÔve? (total numbers); as it seems best to look at our main outcomes (specific aims per our protocol) among patients who initiated ART (and were ART-naÔve at the time of entering the longitudinal cohort)? Almost 95% are HAART naive at baseline.
  • Aims: Compare rates of proteinuria
  • Has dipstick measurements on about 80% of patients.
  • An alternative to a longitudinal model would be using a summary measure such as slope for each patient

Raj Keriwala, Fellow, Pulmonary and Critical Care

  • data from EDEN and FACTT trials, part of the ARDSnet.
  • ARDS Acute respiratory distress syndrome
  • Low title volume mechanical ventilation is the only therapy that has been shown to be helpful for ARDS
  • Want to look at vasopressor use over time and compare by cohort
  • retrospective study looking at the use of vasoactive agents in these cohorts and associated characteristics for any identifiable relationships.
  • will be bringing the full datasets in csv format with me tomorrow
  • What will be the best way to organize this previously collected data to facilitate analysis?
    • Discussed developing a set of inclusion criteria for the combined data to get a comparable group
    • Allow for time to implement a policy change by excluding a period of time, say, 3 months, immediately after a change
    • Recommend scripting data manipulation to ensure reproducibility
    • Will need to make sure the appropriate variables have the same variable names, the same variable types, and same variable categories/levels/formats.
    • Our department supports R. There is limited support for Stata. Few department members would be knowledgeable in SPSS.
  • Discussed whether Dr. Keriwala has access to our dept. through an existing collaboration.
  • Based on the data collected, what types of relationships can we look at in both cohorts and compare?
  • Is REDCap the right software to use for this type of project? We think it will not be worthwhile to put all the existing data in redcap.

Jennifer Morse, clinical trials specialist

  • research study on PACU delirium prevalence
  • How do I appropriately deal with the missing pain score values?
  • RASS Score is ordinal data that is broken down into 2 groups (hyper and hypo active) based on clinical significance. Is this appropriate?
  • Would a chi-square analysis be appropriate? Or an odds ratio? Or a logit analysis to compare odds ratio? Are there any other recommendations for performing the analysis?
  • Is there a relationship between pain score and RASS scores (>=1 vs <=0)?
  • Is there a relationship between 1st column (did pt wake up delirious) and gender?
  • Is there a relationship between 1st column (did pt wake up delirious ) and pain?
  • Is there a relationship between RASS scores (>=1 vs <=0)? and gender
  • Is there a relationship between CAM positive and gender?
  • Is there a relationship between CAM positive and RASS ((>=1 vs <=0) and gender?
  • Discussed the characteristics of the outcome.
  • We think it is a bad idea to combine negative scores with zero scores, since both negative and positive scores are "bad." Only two of ~400 are positive. We requested Jennifer bring us the frequencies of the different levels of this variable to aid in the decision of how to model it.
  • We recommend the investigator come to clinic so we can get more information about the clinical hypotheses behind the analyses requested
  • There is a Monday Anesthesiology studio at 4 that may be a helpful resource.
  • Part of the complexity of this analysis is that the CAM and RASS are not simply ordinal.

2013 July 3

John Koethe, ID/VIGH

  • John emailed the group on 6/20; he will briefly present some data collected in Zambia. He has pre- and post-treatment serum levels of several inflammation biomarkers from a cohort of 20 malnourished, HIV-infected adults starting antiretroviral therapy. He would like to get a quote for VICTR biostatistics support.
    • We estimated ~ 40 hours for this project.

2013 June 19

Bill Wester, ID/VIGH

  • Bill emailed the group on 6/17; he would like to discuss his proposed study "Long-Term Renal Outcomes Among HIV-1 Infected and cART-Treated Adults in South Africa" during biostatistics clinic.
  • Aim 1: Determine prevalence and incidence of proteinuria among HIV-infected adults in the pop. Proteinuria is a risk factor.
  • Aim 2: Compare rates and time to development of ESKRD (CKD4) among cases and controls in the cohort, where case/control is having proteinuria.
  • Aim 3: Compare rates of all-cause mortality between cases and controls.
  • Aim 4: Estimate the impact of of cART on preventing adverse renal outcomes.
  • Proteinuria isdefined as having a urinary dipstick of 1+ or greater. The possible values are 0, 1+, 2+, and 3+.
  • Recommend avoiding framing study as "case/control" and instead preserving the information in the dipstick result.
  • Could use creatinine values to impute missing dipstick results. There may be few instances of missing dipstick but nonmissing creatinine.
  • One of the main objectives is looking at the time-dependent relationship between proteinuria and eGFR level/development of an eGFR event.
  • Ideally we would want to use time to event, but the outcome is not regularly measured. This would be an argument for treating this as a longitudinal study.
  • We will estimate the hours required after we have more information about the condition of the data.
  • Existing cohort size ~ 5000.

2013 June 12

Catherine Bulka, Shane

  • sample size calculation for a study on the prevalence of red-green color vision deficiency
  • The proportion of color-blindness in men in the US is about 7%
  • The study is ongoing and is based on a convenience sample of over 300 patients, many of whom are females.
  • Want to estimate the proportion of male physicians at Vanderbilt with color blindness and the proportion among other male providers at Vanderbilt.
  • Can estimate the difference in proportions between the physicians and other providers. Can see if the CI for proportions in each group includes the value for the general population (7%).
  • Can power based on width of confidence interval.
  • Recommend a better sampling scheme. Could use a simple random sample.
  • Want to ensure high response rate.
  • PS software does power and sample size calculations and is provided for free here: http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize.
  • To compare the two proportions, you need many more subjects. Can increase power by making the ratio of physicians to other providers closer to 1:1.
  • Just to estimate the proportion within +-3% if the true value is 7%, you would need about 278. For +-5%, you would need 101.
  • Online calculator is here: http://epitools.ausvet.com.au/content.php?page=1Proportion

2013 June 5

No client investigators.

2013 May 22

Kirk Kleinfield

  • Wants to identify predictors of epilepsy for an abstract
  • Recommend he focus on variables that he has nonmissing data for and that are a priori identified by experts.

Kurt Niesner, VA GRECC, neurosurgery

  • retrospective study of 58 patients who received a fiesta MRI
  • Exposure is compression: no compression, possible compression, and definite compression, and also whether or not the patient's symptoms matched the presentation
  • Wants to evaluatie the efficacy of pre-operative MRI in predicting post-operative outcome in patients with Trigeminal Neuralgia undergoing Microvascular Decompression surgery.
  • This study may be able to evaluate the association between compression, symptomaticness, and outcome.
  • comparing the presence and locus of trigeminal nerve compression during pre-op MRI to early (1st follow-up visit) and late outcome (1 to 5 years post-surgery).
  • Early outcome is improvement in pain within one month, measured on a scale of 1-3 inclusive. Late outcome is Brief Pain inventory, which is on a scale of 0-150 inclusive.
  • Patients are almost all in severe pain before the surgery.
  • We recommend considering whether there are important factors that would likely influence your outcomes.
  • Could run a proportional odds model with the short term pain improvement as the outcome with the compression and symptomaticness as covariates, as well as their interaction.
  • The mentor could apply for a VICTR voucher for biostatistics support
  • discussed the need for a letter of support for amount above $2000.
  • We estimate that this project would require about $4000.
  • The long term outcome is problematic because of the proportion of missingness.
  • To estimate an adequate sample size for this type of study, you need to consider the smallest effect that you consider important.

2013 May 15

Roshi Markley

  • Needs a quote for VICTR biostat support
  • had a design studio with Frank Harrell and Ayumi Shintami who have helped with the design of the study.
  • Looking at hospital free days following assessment of severe aortic stenosis. Not sure about distribution - similar outcome, delirium/coma-free days, is quite bimodal, but not sure if hospital-free days over one year will be similar.
  • Want to identify what factors are associated with lower hospital free days and develop a multivariable model to predict individuals that are at risk of poor outcome, including therapy of TAVR, surgery (gold standard) or no therapy.
  • About 30% mortality rate expected during first year (50% among patients with no therapy)
  • Patients mainly in 80s and 90s
  • Secondary outcomes: one-year mortality, major vascular complications (eg bleeding)
  • Planning to submit manuscript
  • Estimate 70 hours for work, manuscript prep and anticipated revisions

2013 April 17

Jeremy S. Pollock, Internal Medicine, Wes Ely, Mentor

  • Has VICTR biostats request
  • aim of this retrospective study is to determine the incidence and risk factors of delirium in survivors of cardiac arrest treated in the CVIUC at Vanderbilt University. We hypothesize that delirium, as defined by the CAM-ICU, will be present in more than 25% of patients and that use of benzodiazepines, age, shock, and time to return of spontaneous circulation will be associated with delirium.
  • Retrospective data have been collected on all survivors of cardiac arrest who were been treated with therapeutic hypothermia at Vanderbilt since 2008. We will use this database to assess the incidence of delirium and itís associated risk factors in post-cardiac arrest patients who survive to hospital discharge.
  • Statistical analysis will include determining the incidence of delirium in the cohort who survive to rewarming. Baseline demographics will be compared between patients who experience delirium and those who do not. Multivariate regression analysis will be undertaken to determine the association between delirium and pre-defined risk factors (age, use of benzodiazepines, shock, comorbidities, time to rosc, PEA/asystole) and any differences in baseline demographics with p-values<0.10 (assuming the incidence of delirium is high enough to provide adequate power to include these).
  • Want to estimate the incidence of delirium after the first 24 hours of therapeutic hypothermia. Also want to estimate time in coma and number of coma free (alive) days. Also looking at length of ICU stay. Want to identify risk factors for (first transition to) delirium (in logistic regression).
  • Want to address probably all of these research questions in one manuscript.
  • Could address delirium using time to delirium or using a Markov transition model.
  • Would like to request Jennifer Thompson because of her expertise. This would need to be approved by Frank.
  • We estimate this would require about 50 hours of biostatistics support. We discussed the cost sharing information.

2013 April 3

Harry Wright, Otolaryngology

  • Estimate 35 hours of analysis for purposes of VICTR biostatistics support application.
  • See DataTransmissionProcedures
  • Conducting a retrospective chart review for 71 patients who had surgery to remove a skull-based lesion. The surgery places the facial nerve at risk. All these patients had to have a repair procedure.
  • The main question involves the impact of the time between the surgery and the repair on recovery of function. The functionality is measured on a 1-6 scale. Of interest is whether the patient can voluntarily close his eye (3 of 6). All of these patients have values of 3, 4, 5, or 6.
  • Want to consider patient's age, surgeon,
  • The time between the surgeries is thought to be random, which is good for making inference on the impact of time on the outcome.
  • Proportional odds would be a good choice.
  • You don't really have power to adjust for surgeon id with fixed effects.
  • Would want to look at the distribution of the outcome: frequencies of each 3, 4, 5, and 6.
  • If you do think that surgeon id is an important factor on the outcome, you could model this appropriately using random effects (a random intercept for surgeon id). The purpose in your research objective is not to estimate an effect for a particular surgeon, but to appropriately account for it in the model so that your inference about the variable of interest, time, is correct. However, estimating the variance of the random effects may be hard with only five surgeons.
  • Another way to account for this extra variation due to surgeon is to adjust for the surgeon's case mix.
  • May want to adjust for the severity of each case.

Catherine Bulka

  • project on PACU length of stay and regional anesthesia.
  • based on clinic comments decided to use a stratified Cox regression model with sandwich estimator (because some patients in the dataset had more than 1 surgery).
  • Outcome of interest is time (in minutes) to successful discharge from the PACU.
  • Predictors are regional anesthesia (yes/no), ASA Class, duration of surgery, and patientís age based on previous studies that have looked at PACU length of stay.
  • also matched patients on CCS Grouper (based on CPT codes) so that is how the Cox model is stratified.
  • I tested the predictors in the model to see if they met the proportional hazards assumption using 3 methods: looking at them graphically, looking at the Schoenfeld residuals, and including time-dependent versions of the covariates in the model. For all covariates (regional anesthesia, ASA Class, duration of surgery, and patientís age), the proportional hazards assumption was violated.
  • Because I already am stratifying by CCS grouper, I think my best option may be to use an extended Cox model rather than stratifying on even more variables, but Iím unsure of how to interpret the hazard ratios if I use this approach.
  • We think the departure from the proportional hazards assumption may not be too serious. Whether the assumption holds would not impact the interpretation of the results but rather whether the method (Cox regression) is valid.
  • If the Schoenfeld residuals trend toward zero, you need to use an accelerated failure time model.
  • The Cox model tends to fit better for chronic conditions, where the impact of the exposure is more constant over time.

2013 March 20

Catherine Bulka

  • working on a project looking at hypoxemia in children during surgery & 30-day mortality.
  • includes all children who had some type of anesthetic care between 2000 and 2011, so there are some patients who had multiple surgeries.
  • Iím planning on creating a model with a binary outcome for 30-day mortality and using oxygen saturation (a continuous variable) as a predictor along with other predictors like ASA class, age, race, sex, procedure type, pre-existing conditions, etc.
  • Because there are repeated subjects, I was thinking of using a GEE model, but Iím unsure of what kind of correlation structure to use.
  • is using a GEE model the best approach
  • if I do use a GEE model, how do I figure out what kind of correlation structure to use?
  • If data are large enough, should be less critical in choice but recommend Independent correlation structure, especially since some covariates will change over time (from repeated measures).

Roop Gill, Marcia Spear, Plastic Surgery

  • Cranial facial data base (10 yrs). Data is stored in REDCap
  • Age at which head was fixed, type of suture, race.
  • Outcome: re-operations: redone, voids, voids/reshaping, minor and whether there is an optimal age at which to do the initial surgery
  • Need for initial surgery diagnosed anywhere from birth to 6 months of age.
  • Abstract deadline April 3. Applying for VICTR voucher to get the analysis done in time to submit the abstract.
  • Question 1: Is there a re-op or not. Question 2: Does age at initial operation correlate with type of reoperation.required
  • Might be interested in the age at which the re-op takes place
  • For abstract, look at descriptive statistics * VICTR estimate 60 hours

2013 March 13

Jayant Bagai

  • Needs estimate for VICTR
  • Health outcomes research study of veterans at the Nashville VA hospital.
  • The objective is to compare death, MI, stroke, repeat revascularization and major bleeding in 4 cohorts of patients startified by a combination of arterial access site (radial vs. femoral artery) and type of anticoagulant used for coronary intervention (heparin vs. bivalirudin). One of the key comparisons the incremental benefit of a radial-angiomax strategy in reducing bleeding compared with a femoral-angiomax strategy .
  • The 4 cohorts are:
  1. femoral + heparin n=293
  2. femoral + angiomax n= 255
  3. radial + heparin n = 469
  4. radial + angiomax n= 489
  • We know from previously published data that-
    • Radial access reduces bleeding compared with femoral access and
    • Femoral angiomax reduces bleeding compared with femoral heparin use.
  • What is not known is if the combination of radial and angiomax is superior to femoral angiomax and if so by what magnitude and if that translates into other benefits such as reduction in mortality.
  • The following analysis need to be performed-
    • Comparison of baseline variables in 4 cohorts
    • Comparison of outcomes between cohorts in composite and individual outcomes: length of stay, in-hospital death, MI
    • Multivariable logistic-regression models to identify predictors of major bleeding.
    • Cox regression models to determine association between bleeding and death, MACE (major adverse cardiovascular events- composite of death, MI, stroke and unplanned/urgent revascularization), length of stay and readmissions. * Need to consider other systematic differences between the four cohorts.
  • May want to control for number of stents placed
  • Data includes repeats of individuals
  • May be underpowered to detect differences in overall survival.
  • We estimate that this analysis would require about 60 hours of statistics work.
  • Dr. Bagai also has a study involving a device used to apply pressure for venus closure. They want to compare three devices and manual pressure on bleeding, patient satisfaction, cost. They need to know the sample size required to provide adequate power. If the main outcome is bleeding (y/n), then the sample size will depend on the number of bleeds

Alison Kemph, Hearing and Speech

  • looking at contamination in ear molds in children. What is the percent of ear molds with bacteria? Need to know the sample size required to estimate the proportion. If the true proportion is about 0.5, would need about 43 patients to estimate the proportion to precision of +-0.15. If the true proportion is 0.7, you would only need about 36 patients to get the same precision.
  • May want to record the date of the culture and the child's age.
  • We estimate that this project would take about 40 hours of work.
  • If the scope of the manuscript/project is limited to only estimating the proportion with confidence interval, it can be done in 20 hours.
  • See DataTransmissionProcedures

Dupree Hatch, Neonatology

  • Studying adverse events in intubations
  • Describe the rate of adverse events
  • Have new standardization procedure to implement
  • Need power to detect difference between proportions. For 80% power to detect a reduction of 50% or greater if the baseline rate is 30% with 95% confidence, you would need 134 patients per time period.

2013 March 6

Jennifer Morse, Clinical Trials Specialist, Periop Clinical Research Inst.

  • Survey questions sent to Vanderbilt and MTSA and want to compare the results. (Vanderbilt=74, MTSA=58) 88 and 63% response rates.
  • Want to see if there is a difference in how the two groups answered the questions.
  • Survey of nurse anesthesists about opinions on classes.
  • Students were also asked to identify the five most important aspects out of 22 and least important aspects.
  • Plan on making a basic bar graph for each question but am interested if there are further suggestions for organizing the results, or statistical tests that should be performed.
  • Interested in analysis techniques for displaying results for the ranking question: Participants were asked to pick their top 5 and bottom 5. How can this data be presented collectively? Most common answer? Weighted results?
  • A good way to display the individual questions would be a dot plot. You could, for the graph, dicotomize all 22 questions into either highly or critically important and give one "line" with two dots giving this percent, one each for CRNAs and SRNAs. Or you could give the means
  • Can sort the items in the chart by either the CRNA's score (proportion or mean), or the SRNA's score, or sort by the difference between the two scores.
  • Could do Wilcoxon Rank-Sum Test for each individual test to check for a difference between groups (for one time point).

  • Consider: What information do you have on characteristics of survey responders vs. non-responders? What evidence do you have that the response rates were the same for Vandy and MTSA?

2013 February 27

Eileen Duggan, General Surgery

  • Laproscopic vs. open pyloromyotomies in terms of cost, LOS, complications, etc
  • So far have done univariate analyses (t-tests or Wilcoxon, Fisher's)
  • Strongly recommend multivariate analyses (linear or logistic regression) rather than univariate; may need to transform variables (LOS) to fit model assumptions
  • Create list of main hypotheses (research questions) and confounders before doing model fitting, working with mentors and looking at literature to get a plan together
  • 283 patients total, 127 vs. 156 (ten different surgeons; surgery type is nearly always determined by surgeon more than other factors)
  • Might use surgeon as a random effect in mixed effects model - no estimates, but adjust for surgeon

Catherine Bulka, Anesthesiology

  • help analyzing length of stay in the PACU (outcome) and regional vs. any other type (general) anesthesia (predictor) on a large dataset. We have identified potential confounders such as surgical procedure, ASA Class, age, etc. We wanted to try to match patients on CPT code to account for the different surgical procedures. The only way I know how to do a matched analysis is using conditional logistic regression, but the outcome for this analysis is continuous. Is there a way to do a matched analysis using linear regression?
  • LOS has a strange distribution that is best handled by the Cox proportional hazards model. And if you have more than, say, 1% of the patients die (which right-censors their LOS) you can censor them on the day of death. That way you don't give credit for a short LOS for those who died. The outcome then becomes "time until successful discharge".
  • With the Cox model you can stratify on CPT code group with no matching necessary. It would be nice to have at least 200 subjects per stratum if possible, so probably need fairly broad groups of CPT codes.
  • There are 198,712 surgical cases and 3,300 unique CPT codes. Will group the CPT codes into broader categories.
  • Looking at all surgeries at VUMC since 2008. Hypothesis is that patients receiving regional anesthesia will have shorter PACU stay than those getting general.
  • Patients can be included for multiple surgeries, so need to account for within-patient correlation.
  • First step is to check death rate - if more than 1-2%, need to do Cox regression, but harder to control for repeated measures. Poisson regression is another possibility (count number of minutes), possibly including indicator for death in the model but would need to interact that term with everything else.
  • Lots of potential confounders to consider (BMI, surgery type and length, ASA class).
  • Regional vs. general anesthesia is very often decided by type of procedure - not sure you'll be able to tell which is the real cause of any difference. Maybe focus on surgeries where patients have a good chance of getting either type.
  • Only ~20,000 out of 198,000 surgeries had regional anesthesia.
  • Check with anesthesiology collaborators - JonathanSchildcrout, DavidAfshartous, MattShotwell

2013 February 20

Yuri van der Heijden, Division of Infectious Diseases. Mentor: Tim Sterling

  • K08 submitted, score 'in range' for resubmission in May
  • Looking for support from BCC.
  • Drug resistance in TB patients (specifically, fluoroquinolone) in cohort in South Africa. Aim 1: describing yearly incidence of fluoroquinolone resistance 2007-2011. Aim 2: Describe risk factors for FQ resistance, primary of those is HIV status. Aim 3: Death/culture conversion as outcomes.
  • Bryan Shepherd has been involved in pre-grant preparation. Based on that, It does not appear that he needs VICTR funds for pre-grant submission work. We recommend that he first talk with Bryan as to how much would be required from VICTR to perform the analysis. [BRYAN: I have discussed the analyses with Yuri. As part of his K-award he proposes learning how to do these analyses and implementing them with my supervision. Therefore, I will have protected time (%-effort) on his K-award, and he will only need 100 hours of support from the Biostatistics Core, which will be used in case Yuri needs some additional help with specific parts of his proposal. This is, of course, a rough estimate, but is the amount budgeted into his application.]

2013 February 13

Michael Dewan, Neurosurgery. PI: J Mocco

  • developing a RCT comparing seizure frequency and clinical outcome in patients with subarachnoid hemorrhage who are given levetiracetam (treatment) or no drug (control). Plan to observe for 30 days after discharge.
  • Wants to discuss power calculations, randomization, and a couple other topics
  • Currently, prophylactic meds given if patient has history of seizure; otherwise, just physician preference
  • Between 8-20% patients experience seizures after subarachnoid hemorrhage, typically in the first few days. Up to 1/3 of these are before they get to the hospital.
  • Plan to randomize patients with SAH who have not yet had a seizure (documented absence/no history of seizure). Patients with previous history or who seized in the field will be excluded.
  • Interested in incidence of seizure and modified Rankin scale (6-point scale of ability to perform daily functions) at discharge and 30 days.
  • At VUMC, see approximately 200 patients/year with SAH; estimate about 10% of patients have seizures.
  • Ideally want to balance groups in terms of Hunt-Hess score (1-5); suggested minimization randomization using a web program (JoAnn has experience with this using VUMC investigational pharmacy). Stratified randomization also an option, as is basic randomization.
  • Do not currently plan to blind patients or nurses, but don't expect any differences in monitoring. Recommend some sort of placebo and blinding if possible. VICTR may be a good resource for this, maybe using IV formulation rather than pill.
  • Recommend applying for 20-hour VICTR voucher for initial study planning, sample size, analysis plan.

Joyce Cheung-Flynn, Surgery

  • Wants to submit manuscript to Journal of Thoracic Surgery, which requires signature from statistician signing off on statistical methods. Recommended VICTR voucher for someone to look over data, rerun numbers and review manuscript; statistician could be coauthor.

Mick Edmonds, PMI

  • Has data and would like to answer several sensitive questions. Suggested speaking with CQS statisticians, who have expertise in specific types of data/questions. JoAnn can send email to set him up with a contact (maybe WilliamWu ?) and get accurate quote for number of hours involved. In the meantime, plans to apply for 60-hour VICTR voucher to get started.

2013 February 6

Catherine Bulka, Anesthesiology

 
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* NSQIP data -- looking for association between post-anesthesia drug and post-op pneumonia * ~ 1500 patients with 1600 observations. 65 got pneumonia; * Interested in estimating relative risk of pneumonia; how to handle modeling with repeated records for patients * 43 had 2 surgeries * Potentially use frailty model in a survival analysis rather than Poisson. * Some concern regarding who is given the post-op drug and who is not given it.

Lawrence House, Anesthesiology

* Ability of cardiac troponin's to ID those with post-op MIs. * Objective 1: ID variables which are associated with a post-op cardiac troponin ordered * Objective 2: How do cardiac troponin values correlate with MIs, etc. * Does not include cardiac surgeries but does include vascular surgeries. Suggest removing vascular surgeries. * Impact is from a resource uttilization point of view. * There are duplicates in the data. Recommend using the sandwich estimator for SEs in modeling.

William Sullivan,, 4th year medical students

* Database of medical student notes. * Compare last year's and this year's 2nd year medical student notes * Interrater reliability between four coders who will be coding the 'sophistication' of the notes. * Coding anakyzes language using rating from 0 - 2 that assesses arguments and language of arguments on semantic complexity * Will not be able to assess whether the diagnosis is correct * Coders will be trained. * Need sample size requirements; may be driven by time/resource limitations. Need to de-identify the notes so need some estimate on a sample size. * There will be multiple notes from the same student but all from different patients. * Suggest pulling a small sample and looking at inter-rater reliability for estimates on how long it will take and how different the raters are. Rather than use kappa could use intraclass correlation (ICC). * Ultimately looking for publication but also impacting training policy in the SOM.

2013 January 30

Andre Marshall, General Surgery

  • Planning on a logistic regression model
  • Outcome is 30 day readmission after apendectomy for acute apendicitis
  • 1800 pediatric patients, 103 of which had readmissions within 30 days
  • Wants to identify risk factors for readmission. Specifically insurance status.
  • Perforation status is already known to be a factor.
  • Also wants to describe the group of patients who are readmitted
  • Has thirty days follow up for all patients, and is also interested in time to readmission.
  • Insurance is a substitute for socio economic status. categories are are public insurance and private insurance
  • could have interaction between perforation, length of stay, and insurance status.
  • Planning on adjusting for ethnicity. This may wash out the effect of the insurance because they are often highly correlated.
  • We don't recommend computing power after you already have your data. The width of your confidence intervals will give you an idea of the precision your data are giving.

David Young, Resident, Psychiatry Department

  • mentor is Peter Martin
  • Study on detox med in psych hospital
  • Testing hypothesis that response to detox protocol is associated with dx
  • Response is scored as drowsy, irritable, etc. Patients will get several observations over time, and the mode will be taken as the response.
  • possible diagnoses are depression, PTSD, dementia, about 5.
  • Hope to use the patient's response to detox protocol to determine the diagnosis.
  • Can make a frequency table of the combinations of diagnoses and responses.
  • Currently have 100 patients meting inclusion criteria with existing data
  • Can assess correlation without adjusting for other factors using a chi square test.
  • Each person would only be counted once in the table and analysis
  • You can group different outcomes to avoid cells (combinations) with very few (less than 5) observations, but agree upon the grouping with your colleagues on one set of grouping before running the tests

2013 January 23

Steven Goudy, Otolaryngology

  • Working on basic science R01 due Feb. 5 - mice with knockout genes, looking to determine temporal relationship between gene expression and bone/blood vessel development in upper jaw
  • Frank recommended two-way ANOVA design with time and treatment (and time*treatment interaction), measuring bone density, protein expression, etc as outcomes
  • One potential complication is multiple comparisons - want to study up to 25 genes, and calculating sample size for this is complex
  • Emphasize in grant that each mouse is only studied under one condition - no need to account for repeated measurements
  • For grant purposes, can use boilerplate language to specify BCC for stats help
  • Also look at VANGARD clinic in cancer center for genomics assistance

Damon Michaels & Ricky Sierra, Anesthesiology

  • Looking at pain control using nerve blocks after knee replacement; sciatic nerve splits in two above knee, and if it is blocked before this split, can't assess nerve injury
  • New treatment: tibial nerve block; everyone gets femoral nerve block
  • Outcomes: pain control (patient rating) plus use of opioids, measured in PACU and then every six hours for first 24h
  • Previous studies have showed difference in pain scores and ~10mg morphine equivalents over 24h (SD ~15mg)
  • Main outcome for power calculation: total amount of opioids given in first 24 hours (measured in morphine equivalents); however, using this might result in reduced power for ordinal outcome of pain rating scale
  • Preliminary power analyses in clinic - probably 50 patients per group for 90% power, 35 patients will get about 76% power
  • Email Matt Shotwell (matt.shotwell@vanderbilt.edu) protocol, set up appointment for further discussion

2013 January 16

Scott Zuckerman, neurosurgery

  • project polling expert neurosurgeons on how they would treat recurrent aneurysms at 1 year with Dr. J Mocco.
  • N=40 physicians to be surveyed about when and how they would re-treat a brain aneurysm that was treated one year (more or less) prior, and that had certain characteristics (total of 400 permutations of 6 fundamental aneurysm characteristics); how best to construct questionnaire (with all 400 questions, some sort of latin-rectangle-like design, etc.).
  • would like quote

2013 January 2

Yaa Kumah-Crystal

  • Has questions about stata.
  • has longitudinal data on measurements taken before and after an intervention.
  • the times are different for each patient
  • the origin is the time of intervention.
  • your data need to be formatted with one observation on each row, with a variable for id, time/date, and the response variable.
  • Main first question is how to reshape wide to long in stata.
  • The problem may lie in the variable names. For stata's reshape function, it depends on the number of the time being on the end of the variable name. Make your variable name in that form first.
  • Then use the reshape long function.

Joshua Squiers

Over a 6-year period, Vanderbilt Medical Center developed 
multidisciplinary ICU teams to provide expanded coverage to five of 
their adult tertiary care ICUs, including the Surgical Intensive Care 
Unit, the Neurosurgical Intensive Care Unit, the Medical Intensive 
Care Unit, the Cardiovascular Intensive Care Unit, and the Burn Unit.
Currently, within this model one MD intensivist partners with a number 
of ACNPs to provide care for a significantly larger number of ICU 
patients than the MD could usually provide alone. This medical team's 
core consists of ACNP intensivists and MDs intensivists who provide 
billable medical services for ICU patients, in conjunction with other 
ancillary services.

This project is a descriptive observational study assessing the 
association of NP care on critical care patient outcomes. This study 
will utilize the Vanderbilt ICU database to provide data on a variety 
of outcome measures, including mortality and inpatient healthcare 
associated quality indicators. The ICU database contains records from 
more 150,000 ICU patient visits since 2005, and contains descriptive, 
morbidity, mortality, and quality data. In conjunction with the ICU 
database, the Social Security Administration's Death Master File will 
be searched to determine 30 day and 1 year mortality.

This study will utilize a pre-test/post-test design to compare outcome 
and quality indicators in each of the above ICUs. Each ICU initiated 
their NP teams at different times during the past six years. The 
initiation time point will serve as the demarcation for 
pre/post-testing in each of the ICUs. There will be a six month 
washout period following the team initiation to control for the 
Hawthorne effect, and allow time for team establishment. Pre-test and 
post-test analysis will contain all of the patients for that given ICU
1 year prior to the initiation of the NP/MD teams, and following the 
washout period, will include 1 year of post-test data. All data will 
be de-identified prior to final analysis.

Data from the following ICUs will be collected, utilizing the 
following team initiation dates.

1.Cardiovascular ICU (January 2007)

2.Surgical ICU (January 2010)

3.Medical ICU (October 2010)

4.Burn ICU (June 2010)

5.Neuro ICU (August 2009)

Catherine Bulka, Anesthesiology

  • Has a rejected manuscript that needs to be improved
  • Has data from a prospective group that was matched to a retrospective group
  • Research question is whether continuous monitoring of hemoglobin during surgery reduces the number and amount of transfusions.
  • Belief is that without monitoring, surgeons tend to overprescribe transfusions
  • type of surgery is elective orthopedic.
  • since the prospective, controlled trial was not blinded, they later decided to also include retrospective data.
  • One strategy to address this concern could be to compare the rate of transfusion between the retrospective group and the control group. Give the odds ratio for receiving a transfusion with a confidence interval.
  • We recommend the main analysis be only on the prospective patients.
  • For your main analysis, instead of providing a p value from a Fisher's exact test, give an odds ratio with confidence interval.
  • Reviewers are also concerned about heterogeneity caused by different types of surgeries. One way to address this is point to the fact that the groups were randomized and also show the breakdown of types of surgeries by group. (The distribution of types of surgeries don't appear to differ across groups.)
  • The reviewers are asking for p values for the baseline comparisons. This would require a statistical argument that the two group populations are by definition the same, so testing the hypothesis would not make sense, plus some reference. If you do end up doing statistical tests, use wilcoxon rank sum, kruskal wallis, or chi square tests.
  • The original randomization was done by surgery room, not based on surgeon or patient.

2012 December 19

Kelly Green, Cardiology

  • Developing registry (~130 records) for two types of TAVR devices (aortic valve replacements); TAVR is fairly new - approximately 5000-6000 cases performed so far in US
  • Noticed large difference between percentage of white vs. African-American patients in registry
  • Screened 350 patients for procedure, only 15 were African-American, and only two taken for procedure
  • Prevalence of aortic stenosis among African-American population (or other minorities) is unknown
  • Looking to use BioVU, CMS (Medicare) data to compile registry - disease is almost exclusively in elderly patients; however, limited by diagnostic codes' accuracy and fact that population is pre-selected
  • One option could be to collaborate with Jackson heart study (~Framingham, recreated in Jackson, MS); might be better estimate of true prevalence, but this procedure isn't performed in Jackson
  • Main questions: What is the prevalence of aortic stenosis among African-Americans? Is there a racial disparity in how TAVR technology is applied, when adjusting for severity of disease, SES, etc?
  • These two questions require different data, different studies. #1 requires community-based survey.
  • National Cardiovascular Data Registry (NCDR) is in development, but not mature enough to use yet; Medicare Current Beneficiary Survey may be a good bet (age is available), but need enrollment file, ICD9s, procedures - need to get as close to entire population as possible, not just sick patients. Dave Penson at VUMC might be able to help with this.
  • Any major database like Medicare will take lots of time/power to work with
  • Applying for VICTR studio - no estimate of statistician time needed for that

2012 December 12

Scott Zuckerman (working with J Mocco); Cerebrovascular Surgery

Consultants: Sharon Phillips, Frank Harrell

  • Brain aneurysm surgical approaches
  • Coils can impact into the aneurysm. Is re-treatment needed? Open surgery needed?
  • Survey design - 30 people at a national meeting; hypothetical patients
  • May be 30 most experienced in the country; need to achieve nearly 100% response rate for the survey to be useful for the intended purpose
  • Can put confidence intervals around estimates
  • Think about either taking random sample of all possible permutations of patient characteristics or generate systematic combinations of char.
  • Each surgeon could get a different set of patient char. combinations
  • BUT Some combinations may never occur in nature; can use sampling from real cases to populate survey (a different random sample for each surgeon respondent)

2012 December 5

Alex Jahangir, Ortho/Trauma

  • Planning to submit for VICTR RFA
  • Looking at non-trauma joint replacement patients; ortho has highest incidence of rapid response calls, and 31% are from arthoplasties - perhaps due to age and comorbidities of patients?
  • VUMC has funded an NP to comanage - round on patients, manage meds, troubleshoot, facilitate discharge process; want to look at outcomes for a year before vs. a year after NP starts
  • Death, codes, PEs/DVTs, rapid responses, UTIs, length of stay, 30-day readmission, etc all considered potential outcomes along with costs
  • Collaborating with anesthesiology, ICU, finance
  • Suggest applying for mini-voucher from VICTR to help with grant preparation (Li Wang can instantly approve for <5 hours)
  • Eventually looking for manuscript; will need some sort of help with data cleaning (bioinformatics? biostats CSAs? student for data entry?)
  • Start prospective cohort a few months after NP starts to accommodate transition time
  • Account for double replacements or patients with >1 surgery during time frame?

Kaushik Mukherjee & Kendell Sowards, Trauma and Surgical Critical Care

  • Changes to a VICTR project - suggest talking to Li for specifics on biostats involvement with RFA
  • Three main aims: 1) does insulin resistance differ between patients who have diagnosed diabetes, occult diabetes, and stress hyperglycemia; 2) does insulin resistance differ between patients with and without ventilator-assisted pneumonia; 3) does knowing amount of insulin resistance help predict development of VAP over and above other diagnostic factors (x-ray, cultures, etc)
  • Recommend time-varying Cox model for time to VAP, adjusting for varying insulin drip amount or M (multiplier); other factors could also vary over time - medications, nutrition, etc, along with baseline factors (age)

2012 November 21

Chad R. Ritch, Fellow, Department of Urologic Surgery

  • Proposal development for a pilot study: need help for statistical analysis section
  • A pilot study of preoperative enteral supplementation (nutrient shake, similar to Ensure) before Radical Cystectomy (RC) surgery
  • Randomized ~150 cancer patients into 2 groups (intervention vs. control; all patients will get shake for 4-6 weeks after surgery; patients who previously got chemo excluded), primary outcome: number of complications per patient
  • Population averages about 25% patients with complications from surgery (about half of these have >1 complication); anticipate that intervention will decrease by 10%
  • Complications measured using Clavien scale
  • Potentially use albumin levels on day of surgery as primary outcome: main interest is complication rate or count, but with low N and low complication rate in the population, unlikely to have sufficient power to see a difference in dichotomous/few-category outcome; albumin is independent predictor of complications in previous studies
  • Should also collect data on feasibility for future grant application - how many shakes were drunk, etc (working with nutrition center on this)
  • Other potential secondary outcomes: time to first complication, length of stay after surgery, change in BMI or body composition/muscle mass (using dexa scan in nutrition core)
  • Albumin measurements: baseline, day of surgery (~4-6 weeks after baseline), day of hospital discharge post-surgery, 6 weeks and 90 days after surgery
  • Need to find mean and SD of albumin measurements in control population (mean around 3-3.5?); LeenaChoi can help with sample size
  • Also planning a weekly phone assessment of food intake from nutritionist
  • VICTR RFA deadline December 15

2012 November 14

Kaushik Mukherjee, MD, Division of Trauma & Surgical Critical Care

  • Has data from 2005-2011 on insulin resistance and blood glucose control for intubated patients. Stress-induced insulin resistance (IR) increases infection, including ventilator associated pneumonia (VAP). All vent patients are on an insulin drip.
  • Protocol for monitoring blood glucose (BG) has been in place since about 2002. There is a formula used to determine the insulin drip rate (IDR) = [BG(mg.dl)-60]xM. M is amount of adjustment for the drip to maintain a BG level between 80 and 110 (now using 100-130). When M is high, insulin resistance is increasing, so IDR goes up. The IDR is the best measure of IR.
  • The data consists of 314 patients with VAP and no other infection and 4098 patients with no infection. VAP is defined using CDC criteria, a patient has to be on the vent for 2 days before it is considered VAP.
  • The number of VAP infections peaks at about 5 days. After 10 days there is little information, so we would be looking at the time they were intubated to 10 days out.
  • They would like to know if there is a difference in IR between patients who are diagnosed with VAP and those who are not. Suggestions were to look at the change in IR and BG for all patients and to look at time to infection. They would need to adjust for nutrition,(i.e., glose drip, tube or TPN), mechanism of injury, age, comorbidities (a score used in trauma can be used for this).
  • Would like a voucher for biostats support, we estimated ~ 80 hours for this project.

2012 November 7

Moises Huaman, Fellow, Infectious Disease

  • Has a case control study with 4 groups. Cases are extrapulmonary TB, 11 patients
  • Three sets of controls: pulmonary TB, latent TB, and no TB, ~20 per group
  • Want to look at association between groups and vitamin D levels in blood.
  • Have done kruskal wallis and pairwise wilcoxon tests
  • Want to account for covariates, including season of vitamin D assessment, US/foreign born, age, sex, race, ethnicity
  • Recommend against excluding variables based on p-values
  • How to analyze residuals in stata?
  • Could use proportional odds ordinal logistic regression with vitamin D as the dependent variable. This is an extension of the kruskal wallis test. You don't want to group the outcome, vitamin D.
  • Also want to model tuberculosis and assess the impact of vitamin D, adjusting for other factors. You will have to prioritize the other covariates in this model. The number of parameters you can put in the model depends on the number in the smaller group.
  • Could use a propensity score to adjust for the propensity of TB adjusting for many other factors
  • Would like a victr voucher for biostatistics support. We estimate this will require about $3000.
  • Do not require the propensity score to be linear in the model.

Emily Reinke, Sports Medicine

  • http://biostat.mc.vanderbilt.edu/wiki/pub/Main/ClinStat/bbr.pdf Has information about measuring agreement
  • Question about preferred method of establishing agreement. ICC or Bland-Altman? Frank prefers mean absolute discrepancy (within- or between-raters). Zhouwen has programmed R functions for this.
  • Patients who have undergone reconstruction are x-rayed to measure the space between the femur and the tibia of each knee on both the lateral and medial side and compare the reconstructed knee to the normal knee. We have imaged ~260 people of 420 people so far. The knee images are measured unpaired and blinded to outcome scores. Tell-tale hardware is hidden, but drilled tunnels cannot be hidden so blinding to a reconstruction is not complete. 28 pairs have been measured, 12 right knees additionally have been measured.
  • Initially, we had intended to use a single person to do the measurements. For various reasons we want to add a second reader. Before we did so, we wanted to establish that we had reliability between the two. Both readers measured 10 publically available images from the osteoarthritis initiative and then several months later did it again to determine the inter and intra rater reliability of the measurement method with these two readers. We are using Bland Altman to assess reliability. Note, previous evaluation of the reliability of the method has been performed using ICCs.
  • We had an unexpected finding. In our best case scenario, which came from the comparison of the experienced measurer with herself, the limits of agreement for the measurement[noise] and the greatest anticipated difference in the measurements between knees[signal] are roughly equivalent. In all other cases the limits of agreement are greater than the greatest anticipated difference in the measurements between knees. However, we are talking about tenths of millimeters in difference, and despite extremely similar bone orientation on the x-ray images- this is why I thought the publically available images could act as surrogates for the study images- the dissimilarity between the older arthritic knees in the publically available images and the young athletic ACL reconstructed knees in our study images are believed to be potentially sufficient to explain what we are seeing.
  • Is it permissible to take 10 pairs of images chosen at random from the 260 study images, have the two readers each measure them twice, perform Bland-Altman analysis on the measurements, and if the signal to noise ratio is reasonable, measure the whole group as if the 10 were never taken out?
  • We think using some of the actual data to assess the raters' reliability is fine, since it will be only a small subset and the raters are not likely to remember the images.
  • Recommend using more than 10, maybe 20.
  • Mean absolute discrepancy will be in the same units that you are working with, so that it is more interpretable. Measures degree of disagreement. You can get a confidence interval using bootstrap.
  • Bland Altman will show you the amount of discrepancy by the size of the measurement. It lets discrepancies in different directions cancel each other out.

2012 October 31

Frank Virgin, Otolaryngology

  • Working on a cystic fibrosis-related questionnaire study and needs help making sure that it is set up in a way that will allow analysis. Additionally, for the purposes of funding, I want to get a sense for the amount of statistical help I will need to analyze the results.
  • Discussed importance of getting a high response rate
  • Went over codings of particular questions, and advantages of truly continuous visual analog scales as implemented in REDCap survey
  • Bring back a revised questionnaire to discuss at clinic
  • VICTR $2000 voucher is likely to be adequate for analyzing the data

Curtis Baysinger, Anesthesiology

  • Dr. Baysinger is assessing agreement between two devices that assess effectiveness of a nerve block in cessarian delivery.
  • We encouraged Dr. Baysinger to contact Jonathan and Matt to arrange statistical support through an existing collaboration

2012 October 24

Kaushik Mukherjee, Surgery, Division of Trauma and Surgical Critical

  • attempting to determine if there is a temporal correlation between increased insulin resistance (as manifested by an increased insulin infusion rate) and the diagnosis of ventilator associated pneumonia in critically ill trauma patients.
  • Plans to apply for VICTR biostats support

2012 October 17

Irving Basanez, surgery resident

  • Looking at association of hearing loss and school performance
    • Inclusion: 11-12 years old, 492 total
    • Hearing measurement: each child was presented with a tone of 1000, 2000, 4000 Hz, and they were sounded with a volume of 30 dB. If they failed to hear a certain frequency, the volume would be increased to 35, 40, ... 80 dB. After the threshold is established, the next frequency was tried with 30dB. Resulting volume is the lowest volume in either ear.
    • Measurement of success in school: 4 exams (language, math, ...). Total score: # of correct questions divided by the # of total questions for all 4 tests together.
    • Patient characteristics: age, sex, grade, ear exam.
  • Analysis:
    • Linear (or proportional odds, depending on the distribution of the outcome) regression. Outcome: total test score. Covariates: age, grade, volume (at which the sound is heard). Might want to think about interaction term: age*volume (to allow the effect of hearing loss to be different for different age groups). Another possible interaction term: hearing exam * ear exam.
    • We recommend to choose the covariates a priori.
    • Check correlation of [age and grade] and [hearing exam and ear exam] , for example redundancy analysis.
    • Possible non-linear effect of hearing loss on test performance (might want to include hearing as a non-linear effect)

2012 October 10

Sunya Sweeny, orthodontic resident

  • In digital teeth models, a bite registration helps show where the teeth fit together.
  • Wants to find the ideal material for bite registration. The comparison gold standard is the plaster model.
  • The articulation is going to be measured by measuring the distance between the top and bottom teeth in several different places. This will be done on the plaster model and with each material.
  • She is planning on using only one person's measurements. The variability comes from doing the measurements. * possible model: distance from physical ~ location + material + time OR
  • distance from physical ~material + time (with a separate model for each location)
  • Planning on doing about 25-30 measurements with each material.
  • Wants help with design and sample size
  • Topic is comparing the accuracy of interocclusal record materials in articulating digital dental models.
  • Associated literature has shown that laser scanned digital models are dimensionally accurate representations of plaster models. While plaster models can be easily and accurately hand articulated, articulating digital models is less accurate and time consuming, especially when anually or visually articulating them. An accurate interocclusal record could make the articulating process more efficient. No studies have looked at the accuracy different bite registration materials in mounting digital models.
  • Tentative materials and methods: 1 typodont mounted in maximum intercuspation on an articulator, 5 different bite registration materials.
  • Methods: Place 6 vertical interarch markers on each maxillary and mandibular arch (on the first molars, first premolars, and laterals). Control measurements between the interarch markers will be made using digital calipers on the typodont. Typodont will be scanned using Ortho Insight 3D laser scanner to create digital models. Bite registrations will be made on the articulated typodont using the five different types of bite registration materials. (Need to determine sample size of how many of each type of bite registration material needed). Bite registrations will be scanned using Ortho Insight 3D laser scanner to create digital bite registrations. Digital models will be articulated using the digital bite registrations. Experimental measurements will be made between the digital interarch markers using Ortho Insight 3D model viewing software. Results will compare the differences in the interarch measurements from the control group (physical typodont) and experimental groups (digital models articulated digital bite registrations).

Sarah Hill, nursing doctoral student

  • Have an existing collaboration with Pediatric Department. The statistician is Ben Saville: b.saville@vanderbilt.edu
  • possible strategy: one model each for the number utilized during the operation and one model for number utilized post operatively.
  • number of units post op ~ weigh + post op lab values
  • note that the outcome, number of units, is count data. If using a regression model, appropriate options would be poisson regression or proportional odds ordinal logistic regression.
  • project for my DNP studies at VUSN.
  • retrospective chart review from 1/1/2010- 12/31/2011 for patients who have undergone craniofacial reconstruction for the treatment of craniosynostosis. I am specifically looking at the blood utilization with this encounter.
  • Research questions:
    • Is there a relationship between the patient's weight & total # RBC transfusions
    • Is there a relationship between the patient's weight & total # FFP transfusions
    • Is there a relationship between intraop Amicar use and # of transfusions given intraoperatively
    • Is there a relationship between intraop Amicar use and # of transfusions given postoperatively
    • Is there a relationship between the postop PCV and RBC transfusion administration at 6, 12, 18, 24, 36, & 48 hours postoperatively
    • Is there a relationship between the postop INR and FFP transfusion administration at 6, 12, 18, 24, 36, & 48 hours postoperatively
    • Is there a relationship between the total number of transfusions and length of PICU stay
    • Is there a relationship between the EBL (estimated blood loss) and amount of RBC transfusion intraoperatively
    • Is there a relationship between the EBL (estimated blood loss) and amount of FFP transfusion intraoperatively
  • Frank: "Please convert ages to all be in months and take away the "m" after the numbers."

2012 October 3

Thomas Abramo

  • Using near infrared to measure oxygen saturation in brain tissue. Interest in seeing if these measurements are predictive of a CT scan result.
  • The measurements are taken over time, and the hypothesis is that a big difference in the two hemispheres or overall low values are predictive of pathological CT results.
  • One idea is to have blinded clinicians rate the near infrared graphs as pathological (yes/no), and then look at the predictive ability of the ratings of the CT result.
  • Another approach is to get a summary measure. The summary measure could be the variance in each side.

2012 September 26

Emily Bullington, Pharmacy

  • Project is ďPreventing and managing refeeding syndrome in the acutely ill: the importance of electrolyte replenishment.Ē

Gopi Shah, ENT research fellow

  • doing a project looking at the prevalence of hearing loss in the high schools here in Nashville.
  • would like to screen kids and then give them a survey about their perception of hearing loss.
  • question is how to figure out the number needed in order to give the study power.
  • Frank: "This is not a power problem but a precision (margin of error) problem. My approach to this is to solve for N such that the 95% confidence interval for a probability is +-z when the probability is at the worst case (0.5). z might be 0.1. Statisticians at that clinic can help and can discuss this further to make sure what are your needs."

2012 September 19

Juliana Kyle and Susan Hamblin, Pharmacy

  • Protocol for open fractures (bone pierces skin)
  • Main compliance issue is antibiotic duration. There is also an antibiotic rotation. Also a physician could be noncompliant by not prescribing any antibiotics at all. Right now the group is interested in a composite yes/no overall compliant.
  • Want to assess compliance and its relation to outcomes in terms in drug resistance
  • Primary outcomes are infection and resistant infections before discharge.
  • Secondary endpoints are mortality, length of stay, icu length of stay.
  • Have about 200 patients. Rate of infection may be around 50%. Resistant infection is about 20-70% of those, based on literature.
  • Factors that may affect infection: length of stay
  • Analysis strategy: logistic regression with types of compliance, patient factors (diabetes), physician factors, length of stay
  • Limitations: have know way of knowing each patient's prior exposure to resistant organisms
  • May want to consider other sites besides the original site.
  • It would be useful to collect time to infection, but need to consider the uniformity of assessment. May use time to first positive culture.
  • Many issues, better to have a statistician on board. For purposes of a VICTR voucher, we estimate that this would take roughly 50 hours.

Suseela Somarajan, General surgery

  • This is a study on finding the amplitude of gastric slow waves measured in human with different BMI before and after food.
  • For each BMI category, I have only 3 samples, have 9 total subjects.
  • Question: Is there any statistically significant difference in amplitude before and after food in each BMI category - performed Studentís t-test on the data (statistical significance set at p < 0.05). Since the sample size is very small the results are misleading. I need to show the data and discuss it.
  • Gastric slow waves are measured serially. At each time point there are 16 measurements. They take averages at each time point. Before the meal, there are eight observations, and eight observations after the meal.
  • For the question about the amplitude of the waves, you should adjust for BMI. Since there are many time points within each person, the data are correlated.
  • Since the sample size is small, focus on graphical presentation. Could plot each person's trend, with time on x amplitude on y, and show BMI with color. Indicate the time of the meal with a vertical line.
  • If you collect more patients, focus on modeling, maybe with the difference in pre- post activity as the dependent variable. Could also use longitudinal data, but would need to account for correlation within patients, maybe with random effects.

2012 September 12

Jenny Rothchild, Urology fellow

  • Recommended that she plot histograms of each count or continuous outcome (in aggregate) to assess the distribution
  • Recommend controlling for physician variables or physician in a model
  • May need help in analysis, so recommend considering applying statistical support through a VICTR voucher
  • Results of the histograms will help us to recommend models.

Eric Thomassee, Cardiology fellow

  • Would like feedback on the datapoints prior to data collection. The project involves acute pulmonary emboli and all assoicated therapies (surgical, percutaneous, and medical).
  • We looked at his redcap database and made recommendations.
  • We emphasized collecting actual dates rather than, for example, length of stay or three month mortality (yes/no).
  • Found some instances where radio buttons were more appropriate than check boxes, when the categories were mutually exclusive.

2012 August 15

Daniel MuŮoz, Fellow, Cardiovascular Medicine

Background: Patients who are admitted to ED with chest pain are usually given EKG, troponin test and assessed for a cardiovascular risk score. If all these are within acceptable ranges, they are often admitted for stress test or observed in ED, both of which use lots of resources. Rates of MI/CV death within a month, given the patient has acceptable EKG/troponin, are extremely low. Planned "intervention" is discharge to outpatient with stress test within 72 hours, vs. "control" of typical ED/hospital observation. Planned outcome: major CV event within 30 days (CV [or not clearly non-CV] death, MI). Event rate is hypothesized to be roughly the same for both groups at about 1%. Will also collect resource data, like costs.

Potential issues:
  • Very low event rate -> very large sample size required. Possibly include hospitalization for chest pain and/or revascularization as part of "CV event" outcome.
  • Compliance/patient selection - patients would be responsible for getting their stress tests. Could select patients based on insurance or other factors, but that would limit generalizability and possibly create selection bias.
  • Followup: Planning on SSDI for death, phone followup for other event(s).
  • Secondary outcomes: repeat ED visits
My specific question: what might be the approximate target sample size for a potential trial of the following design?
  • 1:1 randomization of low risk patients into one of two treatment arms (ED eval vs Outpatient eval) in which we aim to test a hypothesis about non-inferiority of outpatient eval as compared with current standard treatment (ED eval)
  • Primary endpoint is a dichotomous composite endpoint (Death/MI or no Death/MI at 30 days) with an anticipated event rate of approximately 1% in each arm
  • Level of significance 0.05
  • Desired statistical power = 0.90
To use the most commonly used approach (relative efficacy using odds ratios) can be used as follows. The standard error of the log odds ratio is the square root of twice 1/[np(1-p)] where p = 0.01. The upper confidence limit of the log odds ratio will be log observed odds ratio + 1.96 times this standard error. Setting 1.96 se to log(1.25) if the multiplicative margin of error is allowed to be 1.25 yields n = 2*((1.96/log(1.25))^2)/(p*(1-p)) = 15,586 patients in each group for a total of over 31,000 patients. The large n is the result of the tiny incidence rate.
<-- 
 -->
 p &lt;- .01 2*((1.96/log(1.25))^2)/(p*(1-p)) 
<-- 
-->

2012 August 08

General Surgery, Pediatric Surgery: Brian Bridges, Michael Northrop

  • Examine the effect of new laboratory procedure on the outcome of pediatric ECMO
  • Control: patients in the previous year; Treatment: patients in the current year receiving the new procedure (N=80 each group)
  • The new procedure is to monitor the activity of heparin
  • Outcome: survival to hospital discharge, time to bleed, (heparin and other parameters)

2012 July 18

General Surgery, Pediatric Surgery: Andre Marshall

  • Congenital Diaphramatic Hernia
  • Does placing chest-tube during repair improve mortality at 6 months.
    • Does not have date that mortality was assessed.
    • Recommend collecting date of death, or at least verifying that mortality assessments are made after 6 months (or 30 days).
  • Apply for VICTR support in the amount of $3500

2012 July 11

MPB: Kate Ellacott

  • Main analysis: the effect of weight loss on a biomarker.
  • Population: patients who had a gastric bypass surgery.
  • Main question: would like to get power calculation of how many subjects are needed to detect a difference in biomarker level before and after surgery.
  • Recommendations:
    • define a clinically meaningful difference in the biomarker from the previous literature (instead of putting a difference that you observed in the previous study)
    • main analysis should be based on the same test that the power calculation was done (probably paired t-test)
    • if the meaningful difference is not know, might be a good idea to estimate possible number of subjects that can be collected and calculate power for difference that can be detected in that many subjects

MPB: Ember Sympson

  • Outcome: post liver volume
  • regression covariates: planned volume, day from surgery,
  • get estimate and confidence intervals for coefficients for each of the covariates

Anesthesiology, Paul W. Hannam

  • Looking at how well some three scores predict certain outcome
  • Outcome: morbidity defined as renal failure, organ failure, or death
  • Recommendation:
    • logistic regression with outcome: 1 - had an outcome within 30 days, 0 didn't have an outcome within 30 days
    • run separate regression for each score
    • covariates: ????
    • compute predicted probabilities of outcome, and Brier index

2012 June 20

General/Pediatric Surgery: Andre Marshall, Martin Blakely

  • Looking at adverse event rates among appendectomy patients from two separate trials - difference between two treatments?
  • Interested in funnel/forest plots, getting overall ORs and AE-specific rates
  • Planning to do formal meta-analysis? Need input from CTSA statisticians in terms of number of hours
  • Would a mixed effects model adjusting for trial be sufficient, rather than formal meta-analysis?

Plastic Surgery: Joshua Anthony

  • Data on hand surgery calls from 111 hospitals in TN capable of handling hand emergencies
  • Do availability for hand call, other outcomes differ for medically underserved areas vs. others, or teaching vs. non-teaching hospitals?
  • For outcome of hand surgery availability, recommend logistic regression model: hand call = medically underserved + teaching hospital + urban area (all yes/no variables); similar for other outcomes like hand surgeon availability
  • Very sparse data for level 2/3 trauma centers (1 and 2 hospitals respectively); recommend combining with level 4 trauma centers to compare level 1 (surgeon should be available 24/7) vs. everyone else
  • 20 hours of VICTR help is probably sufficient for a few logistic models, explanations, manuscript edits (emphasized that clean data is extremely helpful in keeping time down!)

2012 April 25

Anesthesiology : Damon Michaels, Jennifer Morse, Lesley Lirette

  • General:
    • Observational study
    • 2 months
    • two groups: residents, attending physician performing regional anesthesia (nerve block)
    • group sizes: 131 (attending physician) 150 (residents)
    • there are repeated measures: there are only (6 residents+one fellow), and 7 attendings. And for each procedure different attendings supervised different residents.
    • it was not recorded what resident/attending did what procedure.
  • Outcome: delay = time when the patient actually arrives into OR minus time when the OR is ready for the patient. Originally wanted to dichotomise the outcome into 5 minutes or less and more than five minutes.
  • Hypothesis: there is no delay
  • Suggestions:
    • keep the outcome continuous
    • if possible, recover the information what resident/attending did what procedure, and put everything into one model
    • or leave it as is and state in the limitations the fact that there are repeated measurements
    • Recommend you look at the data graphically. You can learn both about whether there was a delay and about the distribution of the times. Do two boxplots (one for residents and one for attendings) overlayed with the actual data.

Medical student group: Monika Jering

  • General:
    • looking at the adult APGAR (score based on blood loss) per minute
    • general question: if a drastic change in the score affects mortality (morbidity)
  • Population: 400,000 admissions.
  • Outcome: death or complication within 30 days
  • Hypothesis: A "sharp" drop in the score is associated with higher risk of death (or complications)
  • Suggestions:
    • define a drop as a maximum drop within one minute period
    • For simplicity: use first admission for each patient
    • adjust for baseline, age, length of surgery, comorbidity index, type of surgery, and other mortality

2012 April 11

Medical student group: Beth Greer, Michael Maggart, Neelam Patel, Calvin Sheng, Tenisha James, Cooper Lloyd

  • Children are classified as exceptionally behaviorally inhibitted or uninhibited, as assessed by a questionnaire
  • Children are given an MRI to measure the amygdala size in volume by summing cross sections. There is specific protocol. The person measuring the images will be blinded to the behavioral status.
  • Could do a separate study to assess intra-rater reliability (have the person assessing the images rate some of the images twice in a blinded fashion).
  • May be a benefit to including patients who are more in the middle of the spectrum
  • There is probably a selection bias in the sample of patients who participate in the study, due to the mechanism of accrual.
  • There are other assessments given to exclude patients with other disorders, including autism.
  • The group wants to know about sample size
  • They will supplement their data with data from a separate database. Recommend that a random procedure be used to draw a sample from this database
  • Research question is whether there is a difference in amygdala volume in the two groups.
  • To find the sample size needed, would need to think about what the smallest clinically meaningful difference in amygdala volume would be and also the standard deviation of the amygdala size in the control group.
  • Another way to look at this research question is to use the inhibition score without categorizing, and testing for association with amygdala size. Depending on the way the score is assessed, this could have the advantage of preserving statistical power.
  • Can use the PS software by Bill Dupont. Free download.
  • Ideally would want to use the amygdala as the outcome variable and adjust for the total brain size as well as inhibition score.
  • The amygdala size is hypothesized to influence the behavior score. If you keep the behavior as two groups (inhibited and uninhibited), the appropriate analysis would be a logistic regression predicting the odds of being in the inhibited group.

2012 April 4

John Newman, Voranaddha Vacharathit SOM

  • Research question: Does animation improves comprehension.
  • Hypothesis: Animation improves learning
  • Outcome:
  • We recommend: linear regression with the post-test score as outcome and four covariates: pre-test score, animation (1=yes, 0=no), style (1=visual, 0=experimental), interaction of animation with style (animation*style). The interaction term allows to model different effect size for different styles .

Katie Guess, Stessie Dort, Fayrisa Greenwald, VUSM

  • Population: trauma coordinators in trauma ICU. The researcher are planning to send a survey to 219 trauma centers.
  • Research question: Whether trauma coordinators assess and educate about acute and post traumatic stress disorder.
  • Concerned about: low response rate, the lower response rate the higher the bias might be.
  • Recommendation: set questions in a way that you don't have to categorise. RedCAP allows to have a scroll bar, where you can mark a percent between 0 and 100. Continuous measurements have more power.
  • Output: descriptive statistics for each question of the survey. For yes/no or categorical questions we report to report proportion (out of the whole population in each region), for ordinal or continuous variables we recommend to report medians and inter quartile range. To compare responses between regions we recommend Pearson chi-square test for binary or categorical variables, and Wilcoxon Rank Sum test for ordinal or continuous variables.

Rich Latuska, VUSM

  • Study question: Do patients requiring multiple specialties for care (ortho, neuro, etc) have better/worse comprehension of their injuries and satisfaction with their care?
  • Likely prospective study enrolling trauma patients, with survey given at first clinic visit and followup (3-6 months)
  • To determine required sample size, talk with mentor about what detectable difference would be important (5% difference in patients satisfied vs. 25%...) and how many patients it's reasonable to survey over available time period
  • Keep instrument mostly the same at both time points, likely with some additional questions at long-term followup

2012 March 28

Kevin Carr, Candidate for MD degree

* See attached files

2012 March 21

Michelle Huber, Pharmacy

  • Project is titled: Delirium and pain in the post operative cardiac surgery patient: a retrospective review. IRB: 111619, PI: Chad Wagner, MD.
  • Have 1 month of patient information for my project with hopes to receive 4 additional months for a total of 5 months. I have already data collected (everything is in RedCap) for that first month of data. As a resident I am required to present my research project at a meeting in April. I will not be able to data collect the rest of the patients in time for this presentation but I do want to analyze what I have for this meeting.
  • How to analyze this data for one month? (we can do something very simple, keeping in mind analysis will be on a larger scale once I have all data)
  • The cost to do a simple analysis for the one month of data so that I can have something to present in April?
  • For the whole project of 5 months, how to analyze this data? Need to send an estimate for VICTR funding.
  • Already have data for 84 patients.
  • Retrospective chart review of patients that had cardio surgery and were transferred to CVICU.
  • In first 48 hours after surgery. Looking at pain and CAM scores (delirious vs. not.). Have data on all drugs.
  • Want to see whether uncontrolled pain is a predictor for delirium.
  • One thing to account for is people who wake up from surgery already delirious. In this case, it will be impossible to assess pain.
  • Is there a possibility of working with Jennifer and Amy, who have experience in working with delirium. This would need to be billed through the BCC. Contact Ayumi Shintani to discuss this possibility. ayumi.shintani@vanderbilt.edu
  • Need to define uncontrolled pain. Could leave this variable as ordinal.
  • Probably for the April 20 presentation, given the time constraints and current number of patients, probably a more simple analysis would be appropriate. Expect ~18-28% events, which would limit the complexity of the analysis or model. Use graphs to display the data.
  • We estimate that this project would take about 45 hours over the next year or so with some effort within the next month for the presentation in the end in April, so an estimate of the cost is $4500.

Stan Pelosi, ENT

  • Descriptive study of pediatric patients with auditory neuropathy who get cochlear implants (inner ear implant for deafness)
  • Auditory neuropathy involves a functioning cochlea, but they are still unable to hear.
  • Risk factors for auditory neuropathy from literature:
  • Initial goal is to describe the patient population.
  • Need help in interpreting a previous analysis
  • The output in stata is a an estimate of the proportions of some variables in the data and also a test of whether the proportions were equal. For example, they tested whether the proportion of premature patients was equal to the proportion of non-premature patients.

2012 March 14

Shilo Anders

  • Wants to address reviewer comments:
There should be analysis for trend used for the analysis rather  
than multiple tests with Bonferroni adjustments.

The authors state that less time was devoted to instability and error  
detection.  However, the total number of interventions was  
significantly larger in the 2007 observation period.  Thus the total  
number of instability and error detection events may have remained  
constant, while the team significantly expanded their role in other  
areas.  The nature of the interventions that increased in frequency in  
2007 might well reflect improved acceptance of the tele-ICU system by  
the bedside providers, with better collaboration and expanded requests  
for assistance.  I believe that the manuscript could be improved if  
the authors expanded their analysis of the nature and possible causes  
of the observed distributional changes in tele-ICU interventions.

The minor revision required is to provide statistical evidence on data  
reliability and data validity.

2012 March 7

Dave Janz, Critical Care

  • We are designing a trial of using acetaminophen to treat sepsis. Before conducting a large trial looking at clinical outcomes, we are going to do a smaller one just looking at changes in markers of disease after 2 days of acetaminophen therapy compared to prior to starting therapy. My specific question is in regards to how to power the study to be able to detect these changes. We will be studying isoprostane levels and the mean level in this population is 65.7 pg/ml, SD 67.4 (isoprostanes are not normally distributed in humans). We would like to be able to detect a change of 20 pg/ml in those receiving acetaminophen and also to be able to detect a difference of 20 pg/ml compared to a group that receives no acetaminophen.
  • We are planning a unrelated study trying to determine the reason why patients in the ICU who receive red blood cell transfusions are more likely to die than similar patients who are not transfused. Specifically, it will be an observational study that will measure 21 markers in the blood of transfused patients immediately before transfusion, along with 4 hours after and 24 hours after. There will be no control group. These markers are also not normally distributed in humans and the marker with the largest standard deviation is IL-8 with a mean of 457.6, SD 7641.4. What I would like to be able to detect a change of half a standard deviation in this group. Do I need to account for multiplicity?
  • Recommend getting sample sizes for the different possibilities of the standard deviations of the difference in pre-post drug outcome. 65 may be the upper bound on the standard deviation.

Tom Golper, Nephrology, Department of Medicine.

  • Need a randomization scheme. Two groups. The experimental condition is the vehicle
  • Recommend a randomized permuted block design. with varying block sizes.
  • Want to do an interim analysis based on the outcome during which a decision will be made to terminate early for efficacy or futility. We recommend from the description of the need for an interim analysis, that an interim analysis based on the outcome is not needed. We recommend that after, say, 40 endpoints are observed, the standard deviation be reassessed, and the sample size be re-calculated.
  • Account for the number of patients who die.
  • Must account for the baseline values in the analysis, since someone with a very low level to begin with has less room to decrease.
  • They assume that about one fourth of the patients will have filters that last 70 hours, when the patients will automatically have their filter changed. Thus, there will not be an observed failure time for those patients.
  • There is a censoring issue, both from lasting more than 70 hours, and also people dying.
  • Want to show that the syringe is not inferior to the infusion.
  • JoAnn Alvarez will help with the permuted blocks.

2012 February 29

Steven White, Emergency Medicine

  • Study of an informatics tool to automate access to the state of tennessee prescription monitoring program (PMP).
  • The tool is much faster than the existing tool
  • The primary study objective is to determine whether automated access results in a higher proportion of ED patients being screened for controlled substance misuse.
  • Additional objectives will be whether increased screening results in a decrease of opiate prescriptions at discharge and whether there are any PMP report variables which influence decision to prescribe opiate at discharge.
  • The study population involves all patients discharged from the ED over a two-month period, with tool ON/OFF at two week intervals. During tool OFF periods, clinicians still have access to the PMP using the web browser.
  • The following data is collected from each PMP automated query: number of prescriptions for controlled substances previous 12 months, number of different prescribers, number of different pharmacies, number of opiate prescriptions, number of days since most recent prescription, pain score at triage, one-way encrypted case number, one-way encrypted MRN, one-way encrypted user racfid, data viewed flag, file opened flag. For periods during which the automated tool is OFF, we will obtain only counts of queries without specific patient data.
  • From ED discharge instruction summary (generated from discharge instruction writer), for each patient discharged during both tool ON and OFF intervals, we will have flags for patient-volunteered opiate use at home, flag for opiate prescription at discharge, name and quantity of product prescribed, one-way encrypted case number which will match up with encrypted case number from automated tool query, encrypted racfid of discharge attending.
  • Could analyze this at the doctor level, and have repeated measures. For example, each row in the data could correspond to one shift. You would collect the number of patients the doctor accessed in each of the two systems, and the number of opiate prescriptions the physician prescribed in that interval.
  • We have an existing collaboration with the Emergency Department. See Cathy Jenkins. We think Jonathan Schildcrout works with DBMI.

Andre Marshall, General Surgery

  • See attached document
  • Wants to study readmission in pediatric surgery with retrospective data. Want to see which diagnoses and procedures predict readmission within 30 days.
  • A big question is whether the readmission was caused by the procedure or not. An algorithm could exist that payers use. If you could use that same algorithm in your study, it would be much more objective than having a group in your team decide each one.
  • Jonathan Schildcrout is working on outcomes research.
  • Recommend putting patients that did not get readmitted.
  • If you can't enter all the "control" data into redcap, you need to randomly select them.
  • If you go through VICTR, we estimate that it will take about 40 hours, or about 4000 dollars.

2012 February 22

Mei Liu, Biomedical Informatics

  • Retrospective study. Outcome: Abnormal lab test. Exposure: Certain medication
  • Planning to do propensity score matching to compare each medication group to a control group.
  • Recommended to talk to Michael Matheny (maybe to Jonathan as well)

2012 February 15

Michelle Collins, School of Nursing, and Sarah Star, OB Anesthesiology

  • Planning a retrospective chart review of patients who got nitrous oxide in labor.
  • What patient factors influence success of the the nitrous oxide.
  • Outcome is whether the patient got an epidural.
  • Have about 350 patients over about one year period
  • They think parity, use of oxytocin induction, oxytocin augmentation, length of labor, provider type (midwife, ob, combo). May want to adjust for time since nitrous oxide was made available in the hospital.
  • Nitrous oxide relieves anxiety. Some women may want nitrous oxide but not an epidural because they can more effectively push, and because they have more control on the amount
  • If you include patients that did not get nitrous oxide in the first place, it may be important to include a propensity score for using nitrous oxide.
  • Can email Jonathan Schildcrout to see if there is already a collaboration in place. jonathan.schildcrout@vanderbilt.edu

2012 February 8

Pat Keegan, Urologic Oncology

  • Planning a trial to investigate differences in using staples vs. ties to close blood vessels in prostatectomy.
  • The outcomes they want to compare by are surgical margins and continence.
  • The investigator had already made some sample size calculations in PS and wanted to review them with us.
  • We recommended accounting for attrition in the sample size
  • We recommended collecting the actual measurement of the margins rather than only whether they were positive, if feasible.
  • Recommend collecting the patient-reported number of pads used per day rather than only collecting three levels: 0-1, 2-5, 5+. The number of pads can always be grouped later if necessary, but if you only collect the three groups, there will be no way to obtain the actual value.

2012 February 1

Blake Hooper (Anesthesiology)

  • Evaluate two new cath methods compared to gold standard; cardiac ouput as outcome
  • 15 subjects, A was performed on all 15 subjects, B was performed on 5 subjects, gold standard from all the subjects
  • measurements of different methods were made at the same occasions for each subjects; the number of measurements ranged from 8 to 12 for the subjects.
  • Expect the diff between methods and gold standards change with time; non-monotonic trend is expected.
  • Mixed effects model
  • Apply for a Voucher $2000 (http://www.mc.vanderbilt.edu/victr/pub/message.html?message_id=139)

2012 January 25

Lucy He (Neurosurgery)

  • Looking for risk factors for Grade 1 vs. Grade 2 meningioma (diagnosed via radiograph)
  • Outcome is Grade 1 vs. Grade 2 tumor; currently have 128 patients, 94 Grade 1 and 34 Grade 2
  • Limits degrees of freedom possible for well fit multivariable regression
  • A priori chosen potential risk factors include edema (four categories), necrosis (yes/no), and location (four quadrants)
  • Eventual goal is prediction model
  • For now, getting more data probably not practical
  • Suggested VICTR application; either logistic regression (possibly using just edema and necrosis - 3-4 df max), or possibly using propensity scores or other data reduction techniques

2012 January 18

Todd Morgan

  • Looking at patients with metastatic kidney cancer who have undergone nephrectomy.
  • Want to see if the tumor pathology data predicts survival. Also lab values. The want to adjust for type of chemotherapy.
  • They have run a Cox regression
  • They want to create a nomogram
  • Want to look at the predictive ability of the model. Suggest internal validation using bootstrap.
  • Also want to validate a prior model that only uses preoperative data.
  • Have 63 events out of 88 records.
  • Suggest adding the post-op data to the pre-op model and assessing the contribution of the additional variables.
  • Discussed ways to account for chemotherapy in the model yet adequately capture the difference between different chemotherapy regimens. All of the current therapies are similarly not very effective. They aren't interested in quantifying this effect; they want to control for it.
  • In validation, also need to validate the model selection process.
  • Need to check the prop hazards assumptions.
  • Recommend at least 40 hours to complete this project.

2012 January 11

Stephen Kappa

  • Looking at cost in cystectomy in bladder cancer, comparing laproscopic vs. traditional surgery.
  • Recommended that all dollar amounts are adjusted to one standard across years using the consumer price index.
  • Talked about approaches for getting biostat support: Check with Tatsuki Koyama to see if this project is covered under existing grants. Another option is a VICTR grant.

Eric Gehrie, pathology resident

  • Looking at abo incompatibility in heart transplant
  • Hypothesis is that poor outcomes (death within a month) are correlated with amount of incompatible antigen in the transplant heart.
  • Have data from 18 cadavers.
  • Has tested amount of antigen in the hearts using two ways: one is tissue staining and categorizing into 3 levels by a pathologist; the other is scoring by a machine the percent positive.
  • Wants to see whether the amount of antigen is associated with factors like gender, BMI, and time between death and autopsy.
  • Recommend displaying all the data graphically.
  • To look at the variability, make a strip plot of the percent positive variable. This will show how variable the data is compared with the median.

2012 January 4

Kassatihun Gebre-Amlack, School of Medicine and Department of Anesthiology

  • Want to understand characteristics associated with transfer to ICU.
    • Outcome is transfer to ICU for any surgical procedure (non-thorasic).
    • 763 required transfer from floor. 63,000 did not require transfer from floor.
  • Predictors of interest include: past medical history (diabetes, cad, cholesterol, renal function), demographics, surgical procedure (length, medicine, heart rate, rescue).
    • Consider logistic regression model conditioning on all covariates to model probability of transfer to ICU -- single (1 predictor) and multivariable (>1 predictor)
    • Surgeries with longer hospital stays may see more transfer to ICU, will include length of stay.
    • Five years of data, have protocols for transfer to ICU changed? Consider including date of procedure to adjust for this effect.
  • Could consider a combined endpoint of death/transfer to ICU to identify patients at risk.

2011 November 30

Rebecca Snyder, Department of Surgery; Martin Blakely is the mentor

  • Wants to estimate the agreement between three raters of an ordinal scale. There are four levels.
  • Also want to know if the agreement varies for different types of surgery.
  • Recommend using a weighted kappa for the measurement of agreement.
  • For the different types of surgery, they could get separate kappas for the different surgery types (depending on sample size within the types) or do a logistic regression predicting log odds of at least one disagreement between the three raters, including type of surgery as a predictor.
  • Wants to know the sample size required. Recommend estimating sample size based on the precision of the kappa statistic.
  • Estimate that a $2500 VICTR Biostatistical support request should be sufficient

2011 November 16

Robert Kelly, General Surgery, Bariatric DIvision

  • Wants to look at agreement between biopsies taken from different sections of the liver in surgery patients.
  • Each of the biopsies get an ordinal score with 6 levels (majority are in 4 categories)
  • Two sections will be taken from each patient.
  • We recommend that the evaluator of the specimens be completely blinded to the location of the specimen and also the identity of the patient.
  • Wants a refined estimate of number of samples they need.
  • Think about what difference in measurement would be clinically significant.
  • Could calculate sample size required to estimate a weighted Kappa statistic with a 95% confidence interval of a certain width.

2011 November 9

Nick Burjek, Ryan Hollenbeck

  • Retrospective study of 150 patients
  • Outcome variable is good vs. bad neurological outcomes: Outcome comes in a 1-5 ordinal scale: CPC scale at ICU discharge
  • Want to assess prognostic value of bispectral index, which measures brain function and also amount of sedation
  • They have many patients who are on both fentanyl and versed
  • One option for the measurement of bis is time under level of 40.
  • Probably need to incorporate an interaction between bis and sedation requirement.
  • Discussed different ways to summarize the predictor variables.
  • Possible summaries could be slope, intercept (where the started), final level, area under the curve, minimum, time less than 40. (Some of these could be used for either sedation of bis.)
  • Encourage to look at plots of raw bis and sedation data over time for each patient.
  • Can evaluate the effects of many variables in one regression model
  • Want to apply for VICTR statistics support. We estimate that $3500. OR, they may have access to Jonathan's group through an existing collaboration.

Khensani Marolen, Anesthesiology

  • Prospective study on patients going in for surgery. Collecting data on all drugs the patient is on.
  • Two sources of data: one the patient enters at home, and the other is at a clinic visit
  • 160 patients have both sources of data.
  • Want to test the hypothesis that the patient-entered data is more accurate and has more detail and more drugs. We have data that can answer how the responses agree and which source has more drugs, on average
  • Right now excluding OTC and vitamins.
  • Wilcoxon signed rank test and confidence interval
  • Be sure to include limitations of this analysis: assumption that all drugs listed are actually taken by the patient
  • Can interpret point estimate with confidence interval as the difference in medians between the two groups
  • Could also check the actual concordance between the lists
  • The patient self-entry device also collects info on comorbidities
  • Want to increase enrolment mid-study by offering a gift card

2011 Roctober 26

Elizabeth Card, Anesthesiology

  • Want to look at the long-term effects of hypoxia.
  • Have two groups of about 650.
  • Designing a follow-up study to a randomized trial in which all patients were were monitored constantly for blood oxygen saturation levels, and those in the treatment arm had the monitor data monitored in real time
  • One of the aims is to look at the hypoxia that was detected by the stealth monitor, versus the the hypoxia which was detected by the standard of care, which disturbs the patient.
  • Discussed whether the outcome of interest is the sum of time in a hypoxic state.
  • One main outcome is whether the patient was readmitted to the hospital for any reason within 30 days.
  • One other confounder that could be adjusted for or used as an offset or as a predictor is the length of time that they were on the stealth monitor. We think that the differences in the time on the monitor is independent of the patient's health status.
  • Length of stay should be controlled for in models with hypoxia as an outcome. Also amount of blood loss and sedation medicine. (And everything else that is known to affect readmission for the readmission model)
  • One way to measure the hypoxia is the average amount below the lower limit for health. This is the area under the limit divided by the time monitored.
  • Look at the correlation of the nurses' oxygen saturation measures with the stealth monitor's measures.
  • Could also consider the minimum oxygen saturation reached during the observation period. This could be used as a predictor for the patient health outcomes (like readmission) along with the average amount below the lower limit described above.
  • Could look at the average O2 itself (and variance) rather than looking at it only when it is below 90%, which is the definition of hypoxia.

2011 Roctober 19

Cathy Jenkins, Emergency Medicine

  • Designing a study to compare nurses' assessment of vital signs with a robot's assessment of vital signs on healthy volunteers.
  • Can the robot be compared to just one nurse? If not, how would it be analyzed?
  • Change wording of aims from accuracy to agreement.
  • What patient factors influence the robot's agreement with the nurse's measurements?
  • Discussed ways to measure agreement. +

2011 October 5

Khensani Marolen, Anesthesiology
Consultants: Meridith Blevins, Cathy Jenkins, Svetlana Eden, Pingsheng Wu, Steve Ampah, Students

  • Risk assessment tool -- hospital based versus home based. Two reviewers (anethesiologist) review medical history by tool and nurse practitioner, then score. 160 patients with two scores.
    • 1-6 ordinal (1=healthy to 6=extremely sick).
    • Want to assess the differences between two reviewers in one modality
    • suggest (weighted) Kappa -- agreement between reviewers for a given patient - report confidence intervals {http://www2.sas.com/proceedings/sugi22/STATS/PAPER295.PDF}
    • paired Wilcoxon sign rank - test for difference in distribution of scores between reviewers
<-- 
 -->
library(irr) kappa2(r1[,c("ASA_Score_Experimental","ASA_Score_Control")], weight="equal") 
<-- 
-->

2011 Sep 28

Paul Moore, Susan Hamblin, Pharmacy
Consultants: Frank Harrell, Svetlana Eden, Mario Davidson, JoAnn Alvarez

  • Reducing atrial arrhythmias esp. fibrillation, in trauma patients; role of anti-oxidant therapy (AO)
  • Are applying for VICTR voucher
  • Mainly interested in arrhythmias during trauma ICU stay
  • 7d AO protocol; included if receive >= 72h on the protocol
  • Patients must survive 3d and be in the ICU at least 3d to be in study
  • AKI would have also stopped the protocol
    • How many patients starting the protocol who survived 3d did not finish >=3 3d of protocol
    • Find out how many patients had creatinine < 2.5 at baseline who developed > 2.5 by day 3 that led to interruption of the protocol
  • Retrospective cohort study
  • TRACKS database (trauma)
  • Goal: approximate a randomized clinical trial
    • Time zero (randomization time in RCT) patients start
    • All patients included in analysis
    • Patients are similar between 2 treatment groups or you know the measurements needed to adjust for to make them similar
      • Survey 10 experts not involved in the study, have them list all the reasons for starting such a protocol
      • They must be blinded to the database content
      • Verify that all the variables so named are available from the database or you have variables highly correlated with the needed variables
  • Verify that 100% of persons who qualified for getting the protocol actually got it or that the reasons for not doing it are captured with available variables (or if reasons were random)
    • Can't have comorbidities being the reason for not putting someone in the protocol
  • Need to be accurate in identifying arrhythmia/non-arrhythmia
    • Random sample of 100 patients - read charts to check concordance with ICD9-determined presence of arrythmia
  • Recommended analysis: multiple regression (logistic regression model if outcome is yes/no)
    • Adjust for potential prognostic variables, especially those in the list assembled from expert opinion
  • There is some value in using time until event (censored at discharge from ICU and possible death) because this recognizes that a patient in the ICU a short time has less opportunity for the arrhythmia
  • Do we want to define time zero as the end of day 3?
    • Assume that arrhythmia that happened day 1 - day 3 should be ignored
    • How to account for arrhythmias within first 3d?
  • Verify that arrhythmia surveillance/coding is constant over the years (also the use of APACHE etc.)
  • A good way to justify the sample size is to estimate the number of qualifying patients on each of the two treatment arms, and the overall proportion with arrhythmia, then compute the margin of error (e.g., half-width of the 0.95 confidence interval for the difference in two proportions; fold-change margin of error in estimating a hazard ratio)
  • Suggest applying for regular VICTR voucher, for $4000 of biostatistics assistance (home dept. will need to pay $1000)

2011 September 21

Bret Alvis, Anesthesiology

  • Research question: whether preoperative use of ketorolac prolongs healing of ankle fracture
  • Hypothesis: preoperative use of ketorolac DOES NOT prolong bone healing
  • Data collected:
    • recovery within one month (yes/no)
    • drug use (yes/no) * recommended: to use PS

Chad Wagner, CV ICU

  • Retrospective study. Would like to look at association of time to delirium and 1. pain, 2. different medications.
  • Concerns:
    • pain scale is subjective and depends on many characteristics (age gender) it's important to adjust for these characteristics.

2011 September 14

Michelle Collins, SON

  • Among women with a positive pap smear result, is positivity of biopsy different by hormone usage?
    • Exclusion: Menopausal, Pregnant
    • Inclusion: PAP smear with LOW or HIGH grade result
    • Outcome: Biospy with NEGATIVE, LOW or HIGH grade result
    • Predictor: Hormone Group (Progesterone (IUD or injection), Control)
  • Four possible research questions (need to clarify interest in 'overcall' versus 'false positive'):
    • Is there an association between progesterone condition and LOW or worse grade biopsy for women who screen with LOW or worse grade pap smear?
    • Is there an association between progesterone condition and HIGH grade biopsy for women who screen with LOW or worse grade pap smear?
    • Is there an association between progesterone condition and LOW or worse grade biopsy for women who screen with HIGH grade pap smear?
    • Is there an association between progesterone condition and HIGH grade biopsy for women who screen with HIGH grade pap smear?
  • Null hypothesis: no difference in biopsy result for different hormone groups
    • test this hypothesis with a chi-square test
    • model the odds of positive biopsy for each hormone group using logistic regression; can adjust for potential confounding (e.g. age, progesterone exposure)
      • this requires dichotomizing biopsy result (LOW and HIGH) vs negative
    • consider modeling the proportional odds of positive biopsy for each hormone group using ordinal logistic regression
      • this preserves the ordinal nature of the outcome variable
      • consider an interaction term with PAP smear type and hormone group
        • biopsy = PAP x hormone

2011 August 31

Jim Phillips, Anesthesiology

  • Wants to look at sedative for pain management and safe for sickle cell patients with acute pain crisis in ED
  • Wants to see the blood's effect on 5 biomarkers
  • 30% reduction in pain is considered significant
  • Safety outcomes
  • Wants to design a single arm unblinded pilot study
  • Advised that he might have biostats support through an existing collaboration plan
  • Wants to know sample size
  • 3 to 4 patients may be available for consenting every week. Study needs to be over by next summer.
  • Pain management is main outcome. It will be measured three times for the patient.
  • Could you obtain a control group?
  • Recommended checking pain distribution in other studies.

Wonder Drake, Medicine

  • Want to study immune response in patients with a lung disease compared to healthy controls.
  • Needs to know sample size for grant. Has preliminary data.
  • Main outcomes are measured by flow cytometry and western blot.
  • The important numbers from the previous data are the standard deviation of the outcome.
  • We recommended looking at the PS software and showed some scenarios.
  • If there are more complicated relationships you need to use a model for, you will probably need even more patients than the number calculated by PS for a t-test.

2011 August 24

Jeff Waldman, anesthesiology

  • Research question: whether the use of PVP (peripheral venous pressure), "low PVP technique", instead of CVP (central venous pressure), "low CVP technique", will reduce blood loss and transfusion requirement in hepatic resection.
  • Details: PVP and CVP are correlated and PVP is normally 2 mm Hg higher than CVP
  • Hypothesis: "Low PVP technique" is not inferior to "low CVP technique"
  • Design: double blinded randomised trial. Randomise patients to those whose doctor uses CVP to manage blood loss vs those who uses PVP to manage blood loss.
  • Outcome:
    • blood loss, measured by the volume of blood in a canister measured by 50 cc
    • plus some eye estimate of number of sponges and how blood is in them
  • Concerns:
    • Because PVP and CVP don't agree (2 mm Hg difference) the group with PVP may show less blood loss.
    • Outcome measure (estimate by eye) is a subjective measure
  • Question:
    • how exactly PVP and CVP are related (slope and intercept, mean difference and mean absolute difference)
  • Suggestions
    1. Report CVP or PVP, revealing which one was reported, let medical team make their decisions on an ad-hoc basis
    2. Report CVP or predicted CVP and stratify outcomes by whether real CVP was used
  • Consider VICTR studio

2011 August 17

Patrick Norris, Surgery

  • Wants to apply for biostatistics help through VICTR for writing a grant.
  • Consult on statistical methods and sample size for an R21 submission.
  • Glutamine supplement in trauma patients vs iso-nitogenous placebo.
  • Primary outcome is improved glucose metabolism and stress induce IR
  • Want help with identifying inclusion criteria and sample size
  • May want to select a group of patients for inclusion criteria that the biggest benefit of the intervention is expected
  • One outcome could be the amount of time within range of glucose
  • One suggestion is also to apply for a different grant for a one arm observational study to look at glutamine levels and demonstrate feasibility.
  • We estimate that this work could be accomplished in $2000.

2011 August 9

Thanh Nguyen, Pediatric Anesthesiology

  • Ben Saville was present and we discussed that he or Jonathan Schildcrout may be resources from our department.
  • Looking at emergence delerium after tonsilectomy in children. Wants to look at a combo of two drugs
  • Wants to design a randomized trial to test this, and wants help in sample size.
  • The main outcome is yes/no whether they have emergence delerium, which comes from the PAED scale, which ranges from 0 to 20 and defines emergence delerium if the scale is greater than 10.
  • We strongly recommend letting the original PAED scale, which contains more information and will give greater power.
  • The control group will get a different drug.
  • The literature reports incidence between 10 and 80 percent, but Nguyen estimates that it may be about 40%. The smallest reduction that he considers clinically important is 10%
  • We recommend that he check the literature to find out the mean and variance in the actual PAED, and also how high the scale ranges.
  • We showed him PS software and showed him several scenarios.
  • May want to control for myaps scale, which measured the pre-op anxiety.
  • Planning to set up a redcap database.
  • Discussed stratifying by agitation to make sure the two groups are balanced with respect to agitation (or other important factors).
  • Discussed inter-rater reliability of the PAED scale, and recommended he check the literature for whether there has already been a study on the interrater-reliability of this scale.
  • Discussed pain as an outcome, maybe measured by amount of pain medicine given.

Nick Ettinger, Pediatrics

  • Wants to see if admission times tend to cluster around the times of shift changes.
  • Has data on the 7000 admissions during one year.
  • Start by graphing
  • Need to control for when the patients are arriving in the ED.
  • Could adjust for the number of patients at risk to be admitted at each time, by considering it an offset term.
  • Ben will work with Dr. Ettinger further.

2011 July 27

Raafia Muhammad, Cardiology

  • Discussed how to record survival data: one column for the date, and one for status. The status can be recurred, dead, or neither. If the person is neither, the date column will record their last known date that they were alive. If the person has recurred, the date will contain their date of recurrence.
  • Her main outcome is time to recurrence of atrial fibrillation after ablation procedure. The patients are not considered eligible to recur until after three months after the procedure. After three months after the ablation procedure, if they are still experiencing afib, they are considered as having a recurrence.
  • For the time to event, you will also need the beginning date. This will be the date of the ablation.
  • She also wants to compare those who do and do not have family history of afib among those who have pharmalogical therapy.

2011 July 20

Kevin Sexton, Plastic Surgery

  • Working with Dr. Thayer
  • Wanting to submit a Defense grant that is due 8/25/2011.
  • Wanting Biostats support to (1) review/refine the grant before submission (ie, sample size calculation and statistical analysis plan); (2) determine the details needed for a BCC "contract"; and (2) the actual statistical analysis once the grant is funded.
  • Feel a $2,000 Voucher would be sufficient to review/refine the grant and determine the specifics for the BCC "contract".
  • The BCC "contract" will cover the actual statistical analysis once the grant is funded.

Chelsey Smith, Anesthesiology Summer Intern

  • Health literacy and surgery outcomes
  • Outcomes: length of stay; ER repeats (30 days from hospital discharge)
  • Predictors: measures of literacy
  • Specific aim: determine if there are associations between literacy measures and outcomes
  • Data: all different surgeries; past 10 years
  • Important: many different types of surgeries --- may want to "adjust" for type of surgery or look at only a specific group of surgeries
  • Important: possible multiple surgeries per person in the retrospective data collection timeframe, which could be correlated with each other --- may need to select only one surgery for each patient (depends on the overall proportion of patients who have more than 1 surgery)
  • Important: Elective surgery vs surgeries after being admitted to the hospital
  • Important: will be difficult to tease out coming back because of surgical complications vs coming back because of health literacy issues --- really want to look at those who returned because of health literacy issues
    • In such a large group of data (N ~ 20,000) these numbers may even out
    • Would need to be stated as a limitation of the study

Shilpa Mokshagundam, Anesthesiology Summer Intern

  • Effects of methylphenidate (primary component of ADHD medicine) on time of emergence from anesthesia
  • Population: children on ADHD drugs
  • Four groups of interest: No ADHD, No medication / ADHD, No Medication / ADHD, Medication w/out methylphenidate / ADHD, Medication w/ methylphenidate
    • May want to create more than 1 variable to capture these four groups --- ie, (1) ADHD as No / Yes and (2) Medication as No Medication / Medication w/out methyl / Medication w/ methyl
  • Some children have had multiple surgeries --- may have been on ADHD medication for some surgeries, not on medication for other surgeries
  • Data: 60 kids (w/ ADHD), but ~50 different kind of surgeries; ton of kids without ADHD
  • To adjust for different kind of surgeries: incorporate measures of the different phases of anesthesia --- specifically, measures of induction and maintenance (third phase of "emergence" is the outcome).
    • Also, length of surgery
  • Also: weight, age, current ADHD dosage (or the fact that the child was taking the medication within a specific time frame of surgery)

Karen Kagha, Anesthesiology Summer Intern

  • Time frame: Oct 2004 to March 2011
  • Non-cardiac procedures
  • Time frame covers a period when there were no alerts to clinician to administer beta-blockers; a period when there was a pop-up alert; and a period when there was a hard stop alert (clinician has to either give beta-blocker or give reason why they weren't administered).
    • Pop-ups appear during surgery (ie, interoperatively)
  • Were patient outcomes different between three different periods
    • Thought is that clinicians' compliance should have improved over the three time frames, so patients' outcomes should have improved
  • Beta-blockers given to patients who have specific clinical characteristics
  • Important: need to know (in detail) whether each patient continued to receive the beta-blocker post-operatively if that's when the patient outcomes are measured
  • Thought: exclude emergency surgeries

2011 July 13

Stephen Kappa, Medical Student working in Urology

  • Prospective, randomized, trial comparing stapler vs. ligature during cystectomy surgery for preventing blood loss. Has data from 80 patients. The main outcomes are amount of blood loss and operative time, total device cost, number of additional staples (in ligature group). The number of staples is a cost issue.
  • A clinically meaningful reduction in blood loss is around 300 mL.

  • Another study: prospectively collected radical cystectomy database of ~1000 patients over 10 years. The goal is to look at the overall survival and cancer survival. Want to look at neoadjuvant chemotherapy. They want to look a the frequency of use of chemotherapy in the database over time.
  • Want to know why patients who did not get the neoadjuvant therapy didn't get it. Their question is about how to categorize the reasons why people didn't get the chemo.
  • Question about how to graphically display the usage over time: Error bars suggested to show variation.

2011 June 29

Brannon Mangus, ENT

* Research question: compare two types of surgery: 1) replacing STAPES with a prosthesis or 2) combining STAPES with a prosthesis part using laser. Each surgery was performed on a different population. Type one was performed on 600 patients. Type two was on about 100 patients. We have to compare the outcome and cost.
  • Outcome is the difference between hearing level before and after surgery: less or equal to 5 or greater than 5 decibels.
  • We suggest logistic regression model adjusting for age, gender, race, and the type of surgery.
  • Criticism of this approach: two groups may have different baseline readings (which is not available for the first group). Suggested solution: use current data (second group) only in a regression model with outcome of actual decibels difference between baseline and after-surgery (continuous) adjusted for age, gender, and the baseline reading. If baseline reading isn't significant, we'll feel "better" not having baseline reading in the main model. * Cost analysis. All costs should adjusted to 2010 dollars. Outcome: average procedural cost. Analyse the difference between groups using Wilcoxon Rank Sum test.

2011 June 1

Jesse Ehrenfeld, Damon Michael, Elizabeth Card, Anesthesiology

* First question about how to handle missing data in outcome. Had a non-randomized intervention trial. * Groups assigned sequentially -- those who received standard of care group and then those who received intervention * Outcome was nerve damage after surgery. Outcome was assessed via 30-day follow-up attained from phone calls. * About 20% of intervention subjects could not be reached for follow-up. * Given that it was outcome data missing, suggested describing differences in those lost-to-followup and those with complete information. * Final analysis would need to be on complete cases. * Second question related to vital signs in patients. They have a device that measures the vitals continuously in addition to the values the nurses are taking. They have ~1300 patients and would like to compare the two values.

2011 May 18

Damon Michaels, Elizabeth Lee, Anesthesiology

  • Post operation pain assessment in autistic children who have trouble communicating
  • Want to evaluate a validated tool that assesses pain and discomfort in children who have trouble communicating. This includes parental input. This will be the intervention.
  • The children are all undergoing similar dental rehab surgeries
  • Evaluation using a parent questionnaire
  • Outcomes include pain medicine use, parents survey on satisfaction, length of stay
  • The researchers feel that a 25-30% increase would be clinically significant
  • Recommend controlling for for body mass in the model for amount of pain medicine.
  • Recommended randomization rather than pre-post, but this seems hard logistically.
  • May need to control for medicine received pre and during surgery.
  • Control for autism severity
  • Recommend at least 30 per group, the more the better.
  • Could come back to clinic after around twenty patients have data to recalculate the sample size based on the better standard deviation data.

2011 May 11

Koffi Kla, Stuart Mcgrane, Anesthesiology

  • Hypothesize that nurses in PACU are less experienced in and less likely to have specific training in critical events as compared to nurses in ICU.
  • Will have a lecture in and simulation of a critical event
  • Is doing a pre and post intervention survey in redcap
  • We recommend talking with redcap personnel and making sure that the pre and post surveys can be linked (can match an individual's first response with her second response, although the responses are still anonymous.)
  • Recommend using a visual analog scale (VAS) for overall comfort level in critical situations for both pre and post. That way you can use a regression on the post comfort level, using the pre comfort level as a predictor.
  • If there is an important binary outcome (yes/no), a yes/no answer choice is appropriate. You can use logistic regression for these.
  • Discussed strategies for increasing recruitment.
  • May want to ask each respondent if she is a charge nurse.
  • Interested in comparing the survey result before taking training and after training. The training is focused on educating nursing staff to respond to critically emergent events.

2011 May 4

Tim Geiger, Colorectal Surgery

  • Tim is interested in investigating the association between average intra-op temperature and 1) surgical site infection and 2) length of stay. There are 296 patients and 20% have infection and were primarily discharged within 4 days. Other functional forms of temperature are of interest, but a simplified analysis plan is desired due to presentation deadline (May 14). It is our recommendation that this is doable in 10-20 hours, but Jeffrey needs to be contacted to determine if personnel are available.

2011 April 27

Mike Stoker, Neurosurgery

  • Looking at the association between cerebrospinal fluid leakage and complete reconstruction of suboccipital cranial defects. The main outcome is leakage (Yes/No). There are 100 subjects with one record per subject, and there are 17 events. Reconstruction is a categorical variable Yes/No.
  • Recommendation: to adjust for other variables including previous MVD, age, sex, type of closure (complete, partial, incomplete), and what it was closed with. To avoid overfitting, use propensity score data reduction. To account for surgeon (there are two surgeons), include it as a random effect or to acknowledge in the propensity score.

2011 April 13

Kelli Rumbaugh, Pharmacy

  • Have pre/post data on hemoglobin, hct, for 26 patients on xigris - severe symptom = bleeding.
  • Consider multivariable linear regression if potential confounding exists (e.g. APACHE score).

<-- 
 -->
 Pre_Hgb &lt;- c(10.4,9.7,11.5,13.7,10.6,11.5,12.1,9.3,10.2,9.6,12.5,9.6,6.5,9.8,7.9,8.5,14.5,9.8,7.1,10.2,9.4,13.3,14.8,11.3,8.7,8.7) Post_Hgb &lt;- c(6.8,9.2,10.3,9.9,8.5,9.6,8.7,9.2,8.5,7,10.3,8.6,9.7,7.1,8.8,10.5,9,9.6,11.3,8.3,10,8.4,10.6,9.9,8.1,9.1)

t.test(Pre_Hgb,Post_Hgb, paired=TRUE) diff &lt;- (Pre_Hgb - Post_Hgb) mean(diff)

wilcox.test(Pre_Hgb,Post_Hgb, paired=TRUE,conf.int=TRUE) # results are consistent with t-test

# Null hypthesis: No difference from pre to post xygris # There is sufficient evidence to reject the null hyptohesis of no difference in Hgb from pre to post tx (p=0.008). # The mean difference from pre to post tx is 1.32 dg/ml (95% CI: 0.37, 2.62).

apache &lt;- c(25,27,28,16,27,30,21,30,23,21,32,26,25,30,24,24,25,20,27,26,31,27,32,29,32,27) plot(Pre_Hgb,Post_Hgb) plot(apache,diff)

library(Design) d &lt;- datadist(Pre_Hgb,Post_Hgb,apache) options(datadist="d") # No d -&gt; no summary, plot without giving all details

f &lt;- ols(Post_Hgb ~ rcs(Pre_Hgb,3) + apache, x=TRUE) anova(f) summary(f,Pre_Hgb=c(9,10)) # Pre_Hgb and Post_Hgb have a non-linear association. # The effect of Pre_Hgb on Post_Hgb adjusting for APACHE score no longer achieves statistical significance (p=0.0581). # However, absence of evidence is NOT evidence of absence, so best to quote the effect size (95% CI). # For an individual with Pre_Hgb=9 versus an individual with Pre_Hgb=10, the Post_hgb difference adjusting for APACHE # is -0.37 (-0.74,0). # For an individual with Pre_Hgb=5 versus an individual with Pre_Hgb=6, the Post_hgb difference adjusting for APACHE # is -0.51 (-0.97,-0.05).

par(mfrow=c(2,1)) plot(f) 
<-- 
-->

2011 April 6

Matt Landman, Surgery

  • Is interested in rate of organ donation designation over time. Has county-level data.
  • Poisson regression with a random effects model.
  • Or poisson regression with county and time as a fixed effect and an interaction between county and time.
  • Include county-level covariates, like median income, percent income, etc.

2011 March 9

Joyce Cheung-Flynn, Surgery

  • The project was designed to study the expression level of the HSP27 protein in human saphenous vein remnants obtained from bypass surgery and to determine if HSP27 represents a new vascular biomarker for the metabolic syndrome.
  • Advised that they did not have enough data to run a multivariable regression (n = 11). Recommended scatterplots of the continuous variables by HSP27, and strip plots separately for dichotomous variables, possibly showing another dichotomous variable using color.
  • Excel file: ExcelFile(CheungFlynn_BiostatClinic030911-1.xls)
  • For a separate study, they are applying for VICTR support. One criticism from the VICTR pre-review was that the tests mentioned were incorrect. We reviewed the stats section and improved the wording and made some small tweaks. We also discussed whether a biostatistician will be working on this project. Dr. Cheung-Flynn was planning on doing the analysis herself. I advised to either list herself and give some qualifications for this type of analysis or amend her request to include biostatistical support.

Padmini Komalavilas, Surgery

  • We did some sample size and power analysis in PS software and discussed that these calculations are based on the assumption that the data will come from independent units and that if they get samples from the same patients they will need more statistical methods to analyze it.

Mary Williamson, ENT

  • Is looking at patients with down syndrome who have tonsilectomy they are getting chart reviews. They have 120 records, and so far 15 of them have required a second surgery. They are interested in finding variables that are associated with requiring a second surgery. She is working on a conference presentation at the end of April. We advise that her data would need a lot of work to be analyzable and also that the VICTR statisticians may not be able to accommodate this deadline. We recommended narrowing down the candidate predictors to a handful to avoid overfitting. http://biostat.mc.vanderbilt.edu/wiki/Main/DanielByrne. We estimate that this request can be fulfilled with $2000.

2011 March 2

Ted Towse, Radiology

  • Looking at matched case-control study of ALS patients
  • Two time points: time 0 and 6 months later
  • VICTR review

2011 Jan 12

Kathy Edwards: NIH Multi-center vaccine trial -- Dichotomization issue

<-- 
 -->
 set.seed(1) mu &lt;- 100 sigma &lt;- 20 cutoff &lt;- 125 true.prob &lt;- 1 - pnorm(cutoff, mu, sigma) # .106 xlim &lt;- c(0, round(2*true.prob, 2)) nsim &lt;- 10000 par(mfrow=c(4,2)) for(n in c(100,200,1000,5000)) { p1 &lt;- p2 &lt;- double(nsim) for(i in 1:nsim) { y &lt;- rnorm(n, mu, sigma) p1[i] &lt;- 1 - pnorm(cutoff, mean(y), sd(y)) p2[i] &lt;- mean(y &gt; cutoff) } rmse1 &lt;- sqrt(mean((p1 - true.prob)^2)) rmse2 &lt;- sqrt(mean((p2 - true.prob)^2)) eff &lt;- (rmse1/rmse2)^2 w &lt;- paste('n=', n, ' RMSE=', round(rmse1,4), sep='') hist(p1, xlab='MLE of Exceedance Prob', main=w, nclass=50, xlim=xlim) abline(v=true.prob, col='red') w &lt;- paste('n=', n, ' RMSE=', round(rmse2,4), ' Efficiency=', round(eff,2), sep='') hist(p2, xlab='Proportion', main=w, nclass=50, xlim=xlim) abline(v=true.prob, col='red') } 
<-- 
-->
propeff.png

2010 Oct 6

Laura Chang Kit, Urologic Surgery

Is working on two projects. The first concerns a surgery where an artificial sphincter is placed on the urethra for patients with urinary incontinence. The artificial sphincters are measured in circumference, and available in 0.5mm increments. The size used is the smallest size that is larger than the patient's urethra sphincter. The surgery reduces incontinence, but Dr. Chen hypothesizes that the difference in size between the patient's sphincter and the artificial sphincter is related to the amount of post-surgery incontinence. The incontinence is measured as the number of pads the patient uses per day. This is reported by the patient before surgery and at a fixed time point after surgery. We suggested a regression model with the difference in pad use (pre-post) as the outcome, and the difference in sphincter size as the predictor while controlling for the pre-surgery number of pads and some other factors hypothesized to be relevant. We recommend she apply for a $3000 voucher for biostatistics support for an abstract and manuscript and advise that she will need a letter from her department.

Her other project involves a mesh device which is surgically placed around the urethra in women with incontinence. In some patients, the mesh can erode into the urethra or the vagina. She hypothesizes that there are certain risk factors for the erosion, such as higher BMI and smoking, and wants to do a retrospective chart review to identify these risk factors. The problem lies in getting a group of comparable cases and controls. Vanderbilt has many patients who have had this surgery here, but a very small percent of them (about 2 out of 400) have the event. Vanderbilt serves as a referral center for patients in the region who have had surgery elsewhere, but have had erosion and need treatment. She has 36 records of such patients, but there are no controls to go with them. We discussed why the Vanderbilt control patients may not be comparable to these 36 referred with events from various regional hospitals.

2010 Sept 29

Don Arnold, Pediatrics

Rondi Kauffmann, Surgical ICU

Wants to look at the influence of nutrition has on patient outcomes in the surgical icu. Specifically, the time at which nutrition is given is of interest. We discussed controlling for the amount of food and whether the nutrition was given intravenously or through the stomach. She will need to control for how well the patient is doing upon admittance. This is retrospective chart review. We advised to use the time at which nutrition was given rather than grouping early and late nutrition.

2010 Sept 1

Robert Mercie and Fernando Orvalle, Neurosurgery

Retrospective chart review of about 180 records. Evaluating 14 variables' predictive ability for a binary outcome, hemorage during surgery. The main causal outcomes that they have in mind are size of the lesion and the amount of metabolic agent. There are severely limited by their number of events, which is 6. The size of the lesion is correlated with the amount of metabolic agent.

Rachel Idowu, Gen Surgery

Rachel has worked with Meridith to decide on a sampling scheme for sampling hospitals.

2010 Aug 18

Stuart Reynolds Urologic Surgery

Has data from a survey about pelvic symptoms in adult women. Wants to find out which pediatric urologic symptoms are associated with these outcomes. Has about 600 observations. There were four symptoms, each of which are likert scales from never to frequent. One option would be to make one binary outcome based on whether at least one of these was not "never." This outcome could be modeled using logistic regression. We advised that his number of predictors will be limited to the minimum of the number of events and number of non-events. Also, you could fit a separate ordinal logistic regression model for each of the adult symptoms.

We estimated that this project would require about 20 hours of statistical support and recommended applying for funding through victr.

Osgood, Sexton, Hocking, Surgery

Has redcap data on viability of vein grafts. They have about three different outcomes, but one of them only has 15 observations. We recommended only looking at descriptives for that outcome. We estimated that this project would require 30-40 hours, and recommended that the group apply for victr funding.

John Koethe, Medicine

Has about observations on about 800 individuals of cd4 counts over time. They want to look at the association between BMI and change in cd4 counts over time. We recommended using a regression model rather than categorizing and using a Kruskall Wallis test. Got in touch with Cathy, who works with their department.

2010 June 2

Igal Breitman, Surgery

Has repeated measurements from surgery patients. There are two groups. One got a dietary treatment and the other didn't. We recommended a linear mixed model with a random intercept for the ID variable. Igal is interested in finding if there is evidence for an association between the treatment and three continuous outcome variables: glucose, insulin, and and c peptides.

Rondi Kauffmann, Surgery

Rondi has data with multiple observations per subjects. She wants to assess whether a hormone, estradiol, can predict mortality in patients. Sharon suggested a survival model.

2010 Jan 27

Brad Lindell, Med Student

Discussed a study that will be comparing children who are cancer survivors. One group receives traditional follow-up while the other group receives a more intensive follow-up. They would like to determine if the children in the more intensive follow-up group has a higher understanding of their diagnosis. He can have 200 patients in each group. We performed a calculation in PS to determine the detectable alternative.

Rachel Idowu, Surgical Resident

Performed a survey for trauma. This looked at their understanding of trauma and where they felt their understanding was. She is going to bring the data back next Wednesday and we will look at some logistic regression models. See GenClinicAnalyses#Rachel_Idowu_Surgery

2009 Nov 18

Ken Monahan, Cardiology

Measurement: pulmonary vascular resistance. Gold standard: cardiac catherization. Unit of measurement: WOODS, approximate range: .3 to 20. Normal value is about 1.5. Previous not invasive methods: take ultrasound, then run a regression with gold standard as an outcome and ultrasound as a covariate, then use coefficients to find WOODS given the ultrasound. What is a reasonable sample size. Suggestion by Jeffrey Blume: organize a pilot study of 20-30 patients. Need to know the variability of WOODS and ultrasound, within-subject correlation between two measurements, clinically useful range of the difference. Then estimate the variance of the difference of two measurements and use that estimate to project your sample size based on a confidence interval of this difference. After looking at Bland-Altman plot you may see different variability depending on the value of the measurement (ignore this sentence for sample size calculations. )

2009 Nov 11

Dan Barocas, Urology

  • Prostate cancer and predicting upgrading

Fenna Phibbs, Neurology

  • Studying Deep Brain Stimulation in patients with Parkinson's

Marcus Dortch, Pharmacy Trauma

2009 Nov 04

Marcus Dortch, Chris Jones, Surgical Critical Care

Marcus' clinic implemented an antibiotic rotation system, and they want to look at the effect of this system on instance of MDR, drug-resistant infection. We recommended some changes to his data set-up to include the population at each quarter and the number of resistant infections at each quarter. We recommend that he return to clinic with this data set-up, and we will fit a log-linear regression model with negative binomial distribution. One of the important covariates will be whether the quarter was before or after the implementation of the antibiotic rotation system. Another approach would be segmented regression.

2009 Oct 28

Sanjay Athavale, otolaryngology

Had an article with reviewer comments. We recommended some more explanations on some graphs. We also recommended using a logistic regression model instead of several chi-square tests. For a proposal for a new study, we recommended a statistical justification of the sample size.

October 21, 2009

Rondi Kauffman & Rachel Hayes from Surgery

*Cohort
patients in ICU. Patient are checked for blood glucose level and the amount of insulin given to them is adjusted by a protocol. The protocol gives a "multiplier". Based on this "multiplier", the insulin level is changed. Aim: assess trend in the "multiplier" right before the hypoglycemic event. Recommendations: 1. Start from spaghetti plot. Can try time-variant proportional hazard model, linear mixed effect model (since we have repeated measurements).

October 21, 2009

Yukiko Ued from Surgery

*Cohort
Obese patients having a gastric bypass. Measurements: leptin, 8isoP before and after the surgery. Interested in correlation b/w leptin and 8isoP. Recommendation: Spearman's correlation coefficient for correlation. For looking at the difference b/w before and after surgery use Wilcoxon Signed Rank test.

October 14, 2009

Ian Thompson, Urology

*Prospective Study, 17 patients in each arm, main outcome is blood loss, but about 30% get a transfusion. Need to adjust for amount of fluids received and baseline hematocrit. Goal is to compare two devices used in surgery in terms of blood loss. We have informative censoring. Use a t-test or regression comparing amount amount of blood lost during surgery. Use the amount collected "in the bucket" as the outcome. Overall estimated difference in blood loss.

September 23, 2009

CJ Stimson, MD student

  • Had survival data for patients who had radical cystectomy.
  • Wanted to test difference in hazard functions for male and female patients, since a difference was found in another study.

Rachel Hayes, Informatics

  • Wants to find the best way to determine a cut point incorporating positive predictive value.

September 16, 2009

Jonathan Forbes, Neurosurgery, PGNY4

  • plotted data from the study on MRI characteristics of cerebellar neoplasms * R code for plots #Clear existing data and graphics rm(list=ls()) graphics.off() #Load Hmisc library library(Hmisc) #Read Data data=read.csv('DATA_WHPEDS_FOSSA_NEOPLASM_FORBESJ1_2009-09-16-12-24-20.CSV') #Setting Labels
label(data$mrn)="Medical Record Number" label(data$criterion_1)="Criterion 1: Diffusion Restriction" label(data$criterion_2)="Criterion 2: T2 Hyperintesity" label(data$criterion_3)="Criterion 3: Laterality" label(data$criterion_4)="Criterion 4: Tumor Exit" label(data$dwi_cgm)="Relative DWI Value of Cerebellar Grey Matter" label(data$dwi_cnp)="Relative DWI Value of Cerebellar Neoplasm" label(data$t2_hi_tumor)="Relative T2 Hyperintensity of Tumor" label(data$final_path)="Final Pathology" #Setting Units

#Setting Factors(will create new variable for factors) data$criterion_1.factor = factor(data$criterion_1,levels=c("2","0","-1")) data$criterion_2.factor = factor(data$criterion_2,levels=c("-1","0")) data$criterion_3.factor = factor(data$criterion_3,levels=c("-1","0")) data$criterion_4.factor = factor(data$criterion_4,levels=c("0","1")) data$final_path.factor = factor(data$final_path,levels=c("0","1","2","3","4","5","6"))

levels(data$criterion_1.factor)=c("DWI Hyperintense","DWI Isointense","DWI Hypointense") levels(data$criterion_2.factor)=c("T2 Isointense","T2 Hypointense") levels(data$criterion_3.factor)=c("Hemispheric","Midline/Indeterminate") levels(data$criterion_4.factor)=c("No tumor exit from Luschka/Magendie","Tumor exit from Luschka/Magendie") levels(data$final_path.factor)=c("Ependymoma","JPA","Medulloblastoma","Other","Other","Other","Other") names(data) = gsub("_", "", names(data))

#pdf("dwi.pdf", height=8, width=8) color="gray55" jpeg("dwi.jpeg", width=8, height=8, units="in", quality=100, res=600) boxplot(data$dwicnp ~ data$finalpath.factor, outline=FALSE, border="black", ylab="Relative Diffusion-Weighted Intensity (DWI)") stripchart(data$dwicnp ~ data$finalpath.factor, add=TRUE, method="jitter", jitter=.1, vertical=TRUE, pch=19, cex=.8) abline(h=c(1, 1.35), lty=c(2, 3), col=color) legend("topleft", legend=c("Relative DWI of Cerebellar White Matter", "Relative DWI of Cerebellar Grey Matter"), lty=c(2,3), col=color) dev.off()

  • Second study
    • study on trigeminal neuralgia looking at outcomes when the by the replacement of the bone from the craniotomy
    • craniotomy for pain relief by moving the blood vessel off the trigeminal nerve

September 9, 2009

Sylvie Akohoue, gastroenterology

  • We look into risk factors colon cancer in morbidly obese patients.
  • We collect hormones, bio-markers, weight.
  • Design: 4 groups, with 8-10 subjects in each group, 1 - control, 2 - diet, 3 - exercise, 4 - diet + exercise. They are followed for 12 weeks. The data collected at the baseline and at the end of the study. For groups 2 and 4, the weight data is collected each week.
Analysis
linear regression; outcome- 12-week reading, covariates - baseline reading + treatment group (four levels: see the design). (Find out how SPSS defines reference groups). Analysis of the effect of physical exercise to weight change (for groups 2, 4): look at the average weight for every week and see what group decreases more.

September 2, 2009

Bill Heerman, med peds resident

  • Working on understanding analysis in a paper about a plavix study. The study looked at duration of plavix therapy. We went over some of the concepts: splines, competing risks.

Marc Bennett, Alejandro Rivas, ENT

  • Working on setting up database.
  • Looking at ear surgery patients. Want to track information such as complications. One goal is to be able to compare outcomes with other institutions.
  • TIme variables? They need to follow longitudinally over time.
  • Current software? Underlying structure?
  • Pros and cons of using RedCap. Contact Janey Wang janey.wang@vanderbilt.edu about getting current data imported.
    • Pros: Free. Easy to build. Easily exported to different statistical software.
    • Cons: Not good at handling longitudinal data?
  • Microsoft Access would be an alternative.
  • Ways to directly access the data from star panel?

August 26, 2009

Steve Deppen, Thoracic Surgery

  • BMI vs. resource use in lung cancer surgical resection
  • Research by others has viciously dichotomized BMI
    • relationship may be non-monotonic; need a smooth nonlinear relationship
  • Relating complications to obesity is also of interest but the multiplicity of complications is a problem
    • May be helpful to score complications against some other outcome as has been done by G Marshall, F Grover, W Henderson, K Hammermeister in the cardiothoracic surgery literature
  • Dataset has height and weight so can do a statistical test of the adequacy of the BMI formula in predicting risk, e.g., try adding log(weight) to a model that has log(BMI) in it to check if the coefficients of log(weight) and log(height) have a ratio of 1 : (-2)

July 15, 2009

Raphael See and Lisa Mendes, Division of Cardiovascular Medicine

  • End stage renal disease; nuclear perfusion imaging: dobutamine stress thought to be less effective in ESRD
  • Goal is detecting/quantifying coronary artery disease (CAD)
  • Head to head assessment of CAD detection ability with 2 diagnostic modalities
  • Prevalence of CAD around 0.2
  • Can take as the goal to estimate the difference of diagnostic accuracy of the two methods or alternatively to estimate the probability that method 1 is more accurate than method 2
  • Nuclear perfusion imaging provides segmental perfusion assesssment (17 segments)
  • Stress echo uses wall motion abnormalities in segments (but different segments)
  • Ischemia, infarct, LV function can all be quantified
  • Referral bias: negative tests less likely to lead to cardiac cath
  • Would CT angiography be an alternative? Calcification is a problem.
  • A hybrid design would be to always send to cath every patient who is positive on at least one of the two tests
  • If use a CAD severity index (ordinal scale with 10 or so levels) can correlate each noninvasive test index with this severity scale and assess the difference in two rank correlations (correlated correlations; could use the bootstrap to get the final confidence interval)

May 27, 2009

Kathy Hartmann, Epidemiology/Ob/Gyn

  • Question concerning grant.

Apr 29, 2009

Tom Compton, Biomedical Informatics

  • Studying accuracy of nurses recording of glucose measurements compared to what's recorded on the glucometer.
    • Are the nurses recording accurately?
  • Now studying "multiple levels of inaccuracy"
  • 25 values per day per patient, about 1900 patients in one ICU, approx 90,000 values
  • Is there a relationship between errors and blood glucose variability?
  • May want to consider looking at length of stay, where/when in the hospital are these errors occurring, range of errors.
  • Concern: What if, for example, it's a trauma patient with high variability initially and all of the errors occurred after they stabaliized?
  • Recommended requesting a VICTR voucher.

Apr 22, 2009

John Wood and Brian Burkey, Otolaryngology

  • 110 patients, retrospective study, all possible selected from the late 90's on.
  • Only 2 cases with hand function deficits
  • Looking to create a predictive model of morbidities
  • 3 tests: Could create at 2x2 tables, look at Kappa statistics
  • Created 3 2x2 tables, doppler vs allens; doppler vs surgical allens; allens vs surgical allens.
  • Is somebody with diabetes/coronary artery disease/etc more likely to lose their flap?
  • Recommend getting a voucher from CTSA for more statistical help.

Apr 8, 2009

CathyJenkins, Pediatric Emergency Medicine

  • Measurements of severity of asthma attacks
  • about 90 patients

Patrick Norris Phd, Trauma

  • New way of measuring bone density, doesn't require a DEXA scan, just a CT
  • Some populations commonly get these scans: older women, people on chronic steroids, etc.
  • Have DNA measurements of a group of these people
  • Defense department is interested in this study; finding young men getting stress fractures; not all men are getting these. Is there a genetic explanation?
  • Continuous outcome.
  • Sharon recommends looking at Ordinary Least Squares Model and/or Proportional Odds.
  • Primary analysis: use OLS, but for "clinical interpretation" use PO.

Feb 22, 2006

Patrick, Trauma

  • Relationship between intracranial pressure(ICP) and reduced heart rate(HR) variability
  • Look for non-invasive monitoring of brain trauma patients
  • less variability with ICP - due to low dynamic range?
  • N=146 dinstinct patients, with 4~5 days of data (every 5 minutes), high mortality patient population
  • avoid uncoupling?
  • regress current on previous time point, prediction as a function of time lag, different behavior between live or die. Is it due to difference in HRV or sampling? Informative missing?
  • determine predictors of completeness
  • take out any indices related to completeness
  • wider sampling intervals, use 1 hour interval instead of 5 minutes
  • two completeness indices: % complete and longest gap, use multivariable logistic model to find predictors of completeness
  • therapeutic scoring system, how intensively the patients were treated
  • repeat completeness analysis with and without zeros
  • Identify patients with zeros and ECG measures, and see what zeros are doing
  • Graphs: show some individual patient data

Mar 1, 2006

Mark Kelley and Brian Gray, surgical science

  • Q: relationship between time of tissue out of body and immunohemo characterization of tissue cells, zenograph of tumor
  • Q: quantify chemical stains - different tumors have different stain patterns
  • How to test for trend
  • grow tumor in mice, take biopsy, divided into samples, let them sit outside for a while, do hemochemical stain
  • validatione: a pathologist resident redo a subsample, blinded
  • How to organize the data
  • three outcomes: score, intensity and pencent of positive cells on each sample at each time; 7 tumors, 4 time points, 3 replicates, 8 stains
  • sources of variations: tumor type, samples, time, stains
  • nested block design:
  • estimate trend within the same tumor type and stain method
  • deal with the 3 outcomes separately
  • A: repeated ANOVA, test for time effect

Muyibat Adelani, Vascular Surgery

2 May 2007

Greg Polkowski, Orthopedic Surgery

Sample size for paired t-test using estimated standard deviation of within-cadaver differences from existing data (n=5). Power of 0.9 was used, sample was estimated to be 15 pairs.
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 # peak load x &lt;- c(100, -200, 300, 0, 100) # differences resected vs. control sd(x) # 182 # energy to fracture x &lt;- c(3.7, 0.1, 2.7, -0.2, 1.6) sd(x) # 1.67 delta &lt;- .15*mean(c(20.1, 3.2, 4.5, 20, 22.4)) # 2.1 used 1.5 
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23 May 2007

Wes Ely and Delirium Assessment study group

*Goal
Development of a score for delirium assessment (severity vs accuracy).
*CAM-ICU
based on 4 features. 1,2,3 or 1,2,4 ==> delirium. Derive a point system, assign points to features, validate the scoring system. Clinicians can do at bedside. Feature 1: 0/1, feature 2: 0-10, feature 3: 0-5, feature 4: -5 to 4.
*Questions
Is it for diagnosis or risk stratification? Which patient population? What clinical outcomes - mortality, long time cognitive function, differential response to treatment? Do features have equal weights? *Current databases: VALID Database: 131 patients, daily assessment of ICU for each of the features, followup to max 14 days, about 5.5 days per patient, all ICU patients, Delirium can be on and off over time. MINd/MEND: 210 patients, with short term cognitive outcome, up to 21 days Delirium/Mortality(JAMA): n=275, with six month survival data *Outcomes:death, ICU LOS, Hospital LOS, long term CI
*A
For risk stratification, the scoring system should be outcome specific. Look at the raw profiles of each feature. Look at how big changes in profiles relate to the outcome. Modelling individual features in the regression models, but this is not the same as directly modelling delirium, still maybe useful. Come up a theoretical framework first instead of emprically deriving it. Go back and rescore each of the 4 features, then you have to go through the estimation and validation for each one. Item-response, split sample, psychometics method. When a score is derived, add other features deparately into the model, this is to test the adequacy of other components or information loss.

28 Jan 2009

Parker Gregg, Medical Student

  • Doing a pilot study in Guatemala to study two different methods of treatment compared to both each other and a SOC
  • "Treatments" are different methods of educating patients with STDs to educate their partners
  • Initially considered doing a crossover study with three clinics - two change treatments and a third just does SOC
  • Goal of study is to increase the number of people who need to be treated for STDs by telling patients to tell their partners. SOC of does not include this extra step.
  • Recommended to ignore clinic effect since it is such a short study over the summer - do one treatment in each clinic. There would need to be a good washout period in between treatments, and there is not enough time to do so. Also, all three treatments would be needed at all three clinics to consider this a true crossover study.
  • Need to answer the question "What to measure?" Just a global number showing a patient increase or track individual patients and who they recommend to come back.
  • Can you get information on patient volumes from the summer before to compare?
  • Recommended giving color coded cards (color based on the clinic) to count referrals.
  • Send survey to MarioDavidson once completed.

18Feb2009

Christina Edwards, Surgery

  • Studying how often people go to the ICU after a Whipple procedure
  • Consider a logistic model with restricted cubic splines for OR Time, estimated blood loss.
  • Include other variables such as age, may want to use splines for that variable as well.
  • Examine the ROC curve for the logistic model as a measure of classification ability.
  • Examine pseudo-RSquare and other statistics as well.
  • Fit and examine various models, CAD seemed to be an important predictor.

04Mar2009

Doug Atkinson, Pediatric Critical Care

  • 20 patients, 4 measurements each about 4 hours apart, 1 missing point
  • comparing devices used to get measurements of blood oxygen saturation
  • What statistical tools to use?
  • Is a sample size of 20 enough? Go ahead and do the analysis, see if there is anything conclusive. If the confidence intervals are too wide, may need to add more patients.
  • Make a scatter plot of all 80 points across time.
  • Several scatter plot suggestions: Measurement 1 vs Truth, Measurement 2 vs Truth, Measurement 1 vs Measurement 2
  • Kappa statistic - for first aim
  • Can fit a regression model but need to take into account that data within each patient is correlated.
  • See spreadsheet tips at the bottom of this page.
  • For statistical help, may apply for a voucher through the CTSA.
  • Recommend getting STATA to do statistics
  • Check with department head to see if you have a collaboration plan.

PingshengWu, Diabetes Center

  • Don't worry about Bonferroni adjustments when doing power and sample size analysis.
  • Do false discovery rate analysis at the end.
  • Do one F-test for the ANOVA

11Mar2009

Dr. Meghan Lemke, Pulmonary and Critical Care

  • Has a poster presentation this weekend, question about graphs.
  • If you can get the raw data, email JoAnnAlvarez and she can make some graphs
  • For p-values, use Wilcoxon Signed-Rank Test

Dr. Thomas Pluim, Pediatric Critical Care

  • Case-Control study
  • Recommend propensity scores, can be used in both matching and in analysis

Joyce Cheung-Flynn, Surgery

  • Wants to submit manuscript to Journal of Thoracic Surgery, which requires signature from statistician signing off on statistical methods. Recommended VICTR voucher for someone to look over data, rerun numbers and review manuscript; statistician could be coauthor.
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Older Notes
 

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Notes for Wednesday Biostatistics Clinic

The Biostatistics Clinic on Wednesdays is dedicated to biostatistics applications in surgery, anesthesiology, and emergency and critical care medicine.

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2015 April 15

Eric Wise, Department of Surgery

  • Clinical surgical project.

2015 March 18

Alexandra Fish, Center for Human Genetics

  • I have a question regarding approaches to handling sampling zeros. I had previously conducted an analysis in which I used a likelihood ratio test to determine if including interaction terms substantially improved model fit. I am now trying to reproduce that analysis in a new data set, which contains sampling zeros. When I run the analysis, I am getting a p-value for the LRT, but the program is unable to estimate the betas for individual terms. So, I guess my question is - is the LRT appropriate in this situation? Can I trust the p-value? I am uncertain which of the clinic themes is most appropriate for this question.
  • She is investigating whether the interaction of two SNPs are associated with gene expression. She has fit a model with additive and dominant terms for both SNPs and their interaction; however, out of the 5000 subjects, no one is recessive for both SNPs. We were unsure of how best to approach this; however, we recommended against some suggestions she found while searching for an answer such as simply adding a constant count to all frequencies to avoid cell counts of
    1. We recommended she contact Yaomin Xu.

2015 March 11

Candace McNaughton

  • "Iíd love suggestions about data manipulation, in preparation for planned analyses. I have received data pulled from the EDW that includes multiple BP and other measures per subject and over time; this data needs to be combined with prescription data (also over
time), as well as with a 3^rd dataset that includes measures of adherence to the blood pressure medications."

Henry Ooi, Cardiovascular Medicine Heart Failure & Transplant

  • "We have a large dataset in Stata format which we are planning to analyze. There are duplicate entrys and we would like advice on how to handle this this in Stata without losing data."
For both of today's clients, a couple of links on aggregating/collapsing data in Stata that may be helpful:

UCLA Stata web page with examples
Short example from Indiana University

2015 March 4

Dupree Hatch

  • "I am a Neonatal-Perinatal Fellow with an interest in Patient Safety in the NICU. I am planning to look at unplanned extubations in the context of the NICU with a future study. I have a data set which contains ~80 unplanned extubations in ~60 patients as part of a larger cohort of all infants that have received mechanical ventilation in our unit for the past year. I am hoping to describe the risk factors for infants to have these events. I have a data set which contains time-to event data for all of the infants as well as various demographic and clinical data. My needs for Biostats clinic are: Help with designing the survival analysis with repeated measures for the patients who have had multiple events. Modeling risk of unplanned extubation with postnatal age as the independent variable Quote for how much time and resources would be needed to have formal biostatistics analysis and help with preparation of the manuscript."
  • Ideal scenario would be survival model with competing risks and repeated measures; we are not sure that this exists. Possible solutions: look at time to first unplanned extubation, with death as a competing risk, censoring babies who did not have an unplanned extubation; also look at calculation of ventilator-free days per ARDSNet definition (babies who die get zero VFDs; otherwise, calculated as [time of interest, eg 28 days] - [time on vent or time after unsuccessful extubation, usually defined as extubation followed by death or reintubation within 48 hours]). Possibly look into data reducation techniques like propensity scores, since low number of events (~60) means only 4-6 degrees of freedom included in model, and association between many covariates and time on vent is very likely nonlinear.
  • Current VICTR policies: http://biostat.mc.vanderbilt.edu/wiki/Main/VICTRBiostatPolicies

Lisa Rae

  • "I am in the process of writing a VICTR grant application looking at changes in Plasminogen and the coagulation cascade in burn patients, with a primary outcomes of: development of heterotopic ossification (incidence 1-3% of burns) and serum levels of coagulation factors after injury and during subsequent wound healing. My data will include serum lab values, xrays and photos of the healing wounds (time to wound closure). "
  • Recommend primarily descriptive study (looking at relationship between total burn percentage as continuous variable vs inflammatory markers, eg d-dimer); event rate for HO is so low (1-3% in total population) that it's unlikely we could practically enroll enough patients to get a proportion/CI within a reasonable margin of error.
  • VICTR policies: http://biostat.mc.vanderbilt.edu/wiki/Main/VICTRBiostatPolicies

2015 February 4

Lara Harvey

  • "...a study of BMI and its influence on FSH levels. I have three large deindentified datasets I have obtained from synthetic derivative and would like some help cleaning and compiling the data and entering it into Stata to use."
  • To read in file from SD: "insheet using "filepath"", or Import, ASCII Data Created by Spreadsheet, select file and choose Tab-delimited, OK (using Stata 10)
  • If more complicated data management is needed to find closest BMI/FSH combination, ask synthetic derivative folks where to start (bioinformatics core?) - this would fit in a 35-hour VICTR voucher, but not sure biostats vouchers can be used for data management and graphics.

George DeKornfeld, VCH Pediatric Heart Institute

  • "We are looking at low birth weight infants under 2000kg with complex congenital heart disease. We are attempting to statistically compare the surgical outcomes, in terms of complications experienced, of a group which was treated at initial presentation and a group which was first allowed to mature and grow. Our hypothesis is that it is better to treat earlier. The groups have been divided and the complications have been noted for each patient."
  • Two main outcomes: worst complication (scored per patient) and days on ventilation
  • Days on ventilation is complex due to mortality (average ~32% in population), so will be artificially truncated for patients who die. Need to account for this - look into ARDSNet definition of vent-free days for one approach.
  • Think of potential confounders; birth weight is a major one (include as continuous variable in model). Can adjust for 1-2 confounders in addition to treatment.
  • In SPSS, use ordinal logistic regression (also called proportional odds logistic regression). We caution against Excel for statistics.
  • A VICTR voucher might be helpful if more complicated techniques (propensity score) or a manuscript are desired; see policies here.

2015 January 28

Mitch Odom, 4th year medical student

  • He has a database of a few thousand; baseline testing only (no repeated measures), looking into neurocognitive and symptom scores for young athletes assessed by a computerized testing battery.
  • There are 740 subjects in his data. Each have taken a cognitive assessment (continuous measure ranging from 0 - 100).
  • The primary question of interest is to examine the association between cognitive score, cognitive status (ADHD; LD; ADHD/LD) and hours of sleep the night prior to the exam (categorized as < 7; 7-9; >9.
  • We discussed any confounders that should be included in the linear regression and whether there should be an interaction between cognitive status and hours of sleep.
  • He might find helpful code hints for SPSS at UCLA's web site: http://www.ats.ucla.edu/stat/

2015 January 21

Sarah Greenberg, Research Coordinator and Health Policy Fellow

  • Orthopaedic trauma - looking for help with a linear regression for determining complications in long bone fractures

2015 January 14

Donald H Arnold, Pediatrics and Emergency Medicine

  • The analysis is as follows:
    • Pulse oximeter plethysmograph estimate of pulsus paradoxus (PEP) is an electronic measure we have developed to measure the severity of acute asthma attacks.
    • Predictor variable: PEP
    • Primary outcome variable: FEV1, continuous variable
    • Secondary outcome variables: i. Acute Asthma Intensity Research Score (AAIRS), ordinal scored 0 to 16; ii. Airway resistance, continuous
  • Will fit 3 baseline models and 3 change models. Need to finish analysis (and possible manuscript) before April 2015. Apply $2500 VICTR voucher (~40 hours).

Cesar Molina, Orthopedic Trauma

  • Plans to attend for help with a sample size calculation where the prevalence is low.
  • Submitted manuscript and was criticized about small sample size.
  • Identify risk factors for deep infection and non-union in pts with open distal radius fractures . N=62 (only 1 infection); N=54 were followed to be able to get outcome of non-union (4 non-union)
  • Download PS (Power and Sample Size) software for sample size calculation. Choose dichotomous outcome, prospective, two proportions. For example, two groups will be diabetic and non-diabetic, compare prevalence of deep infection between the two groups. Need 140 diabetic and 140 non-diabetic pts to detect a difference between 15% and 5% with 80% power.

Carolina Pinzon, Surgery

  • Feasibility study on prevalence of BMP7 protein in two groups of women. Have done experiments in animals, but no data in human
  • If the proteins can be measured in human, want to compare the protein levels between groups. Need standard deviation and a clinical meaningful difference to calculate sample size

2014 December 31

Sarah Greenberg, Research Coordinator and Health Policy Fellow

  • Orthopaedic trauma

2014 November 26

Dr. Hernandez, Jennifer Morse

  • Comparing the difference in time between two different endotracheal devices.
  • Patients in operation room. 12/15 succeeded in A, 6/13 in B. Failure was claimed when more than 10 minutes was taken.
  • Could do two-step analysis. 1. examine binary success using chi-square test or logistic regression. 2. compare time difference among success patients using linear regression.
  • Could use all the data (including censored data) and do survival analysis (log-rank test, or cox proportional hazard model).
  • data from multiple sites. Note the differences between sites.
  • The total time can be broken time into three phases. Could analyze the three periods separately.
 

2014 November 19

Michael Kenes and Joanna Stollings, MICU Pharmacy

  • This study seeks to analyze the natural history of delirium. In order to achieve this, the study will divide patients into two cohorts: those with a continued stop of sedation after spontaneous awakening trial (SAT), and those with sedation restarted after SAT. Based on these two cohorts, we will characterize the time to resolution of delirium after SAT and the time needed to remain delirium-free for 48 hours. Additionally, we will assess the time until reappearance of delirium once sedation is restarted in those patients who become delirium-free following SAT.
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  • Given the outcomes and potentially complex data management, we estimate 120 hours for a VICTR voucher for this project.

Silky Chotai, Spine Center

  • I am working on a spine research project and planning to apply for the VICTR grant.
  • Implemented an IT protocol in July 2014 where each part of a standard-of-care protocol is checked off in the EHR system. Want to see if compliance rates and outcomes improved after this IT system was initiated.
  • Part I: Compare compliance rate for standard of care before and after IT system. This lets us determine whether there was any actual improvement in adherence to standard of care before and after IT system was implemented.
  • Part II: Compare outcomes before and after IT system, while adjusting for potential confounders (severity of injury, age, etc - determine based on clinical knowledge). Hypothesis is that protocolizing standard of care will improve patient outcomes.
  • Suggest classifying each piece of the standard of care protocol into three groups: fully compliant with best practice; received best possible care for that patient, even if not ideal standard of care; received care that did not match ideal care for no recorded reason.
  • We estimate 95 hours for this project.
 

2014 November 12

Wes Self, Tyler Barrett; Emergency Medicine

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  • Would like a victr voucher for biostatistics support. We estimate this will require about $3000.
  • Do not require the propensity score to be linear in the model.

Emily Reinke, Sports Medicine

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  • Question about preferred method of establishing agreement. ICC or Bland-Altman? Frank prefers mean absolute discrepancy (within- or between-raters). Zhouwen has programmed R functions for this.
  • Patients who have undergone reconstruction are x-rayed to measure the space between the femur and the tibia of each knee on both the lateral and medial side and compare the reconstructed knee to the normal knee. We have imaged ~260 people of 420 people so far. The knee images are measured unpaired and blinded to outcome scores. Tell-tale hardware is hidden, but drilled tunnels cannot be hidden so blinding to a reconstruction is not complete. 28 pairs have been measured, 12 right knees additionally have been measured.
  • Initially, we had intended to use a single person to do the measurements. For various reasons we want to add a second reader. Before we did so, we wanted to establish that we had reliability between the two. Both readers measured 10 publically available images from the osteoarthritis initiative and then several months later did it again to determine the inter and intra rater reliability of the measurement method with these two readers. We are using Bland Altman to assess reliability. Note, previous evaluation of the reliability of the method has been performed using ICCs.
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META TOPICPARENT name="Clinics"

Notes for Wednesday Biostatistics Clinic

The Biostatistics Clinic on Wednesdays is dedicated to biostatistics applications in surgery, anesthesiology, and emergency and critical care medicine.

Added:
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2014 November 19

Michael Kenes and Joanna Stollings, MICU Pharmacy

  • This study seeks to analyze the natural history of delirium. In order to achieve this, the study will divide patients into two cohorts: those with a continued stop of sedation after spontaneous awakening trial (SAT), and those with sedation restarted after SAT. Based on these two cohorts, we will characterize the time to resolution of delirium after SAT and the time needed to remain delirium-free for 48 hours. Additionally, we will assess the time until reappearance of delirium once sedation is restarted in those patients who become delirium-free following SAT.

2014 November 12

Wes Self, Tyler Barrett; Emergency Medicine

 
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Examining associations of 2 hour rate control in the Emergency Department with various genetic variants.

2014 November 5

Mary Van Meter, Medical student

I am a 3rd year medical student in the planning phase of a research project that will look at the cost of sterilizing surgical trays within different specialties of gynecology. This project is not really going to be focusing on patient outcomes, and I wasn't sure if I should plan to attend a clinic on Monday because it focuses on cost outcomes of various surgeries, or if I should attend on Wednesday as it involves surgery, or if it even matters at all. I would like to attend the week of November 3-7 if possible.

Kyla Terhune, Department of Surgery

Have not been to a biostats clinic before so new to this, but would like to bring two very simple projects to the clinic:

Both on surgical education:

1) A completed project in which we assessed 50 of our interns (assessment was done by both residents and attendings). Wanted to review the assessments and query the best way to compare the assessment done by residents compared to that by attendings. (I can send data in the morning for review)

- comparing knot tying and suturing skills between two groups ("novices" and R2's)

- interested in evaluating inter-rater reliability (use a Kappa statistic, need to decide if a weighted Kappa is necessary)

- encouraging using two raters rather than three, better interpretability

- if any variables are continuous consider intraclass correlations (should focus on Kappa, Likert variables)

- Need to consider:

- how to define a clinically significant difference

- cautioned on the possibility of high variability in the small data set for the second group (n=8)

- consider randomization within the population of the "novices"

- estimate 40 hours of statistical support

2)Review the study design for an upcoming project that we intend to complete on video assessment of interns and basic technical skills. We have submitted the IRB for approval, and have the basic study plan in mind but wanted to review the study design prior to beginning, and would potentially apply for a VICTR grant with this one.

- also consider using spaghetti plots to compare scores between raters

- estimate 60 hours of statistical support

2014 October 29

Jonathan Schildcrout and Yaping Shi

2014 October 22

Jennifer Morse, Perioperative Clinical Research Institute

  • We created an educational module for 7 fellows who answered daily questions to prepare them for their board exam. We would like to compare their daily quiz participation and scores with their final exam scores. Data to be collected includes:
    Quiz Data
    Number of questions attempted
    Number of questions answered correctly
    Above data broken down by question category

    MCCKAP Data: scores broken down by question category

    Fellowship Data
    Number of hours worked during the specified time period
    Number of procedures performed
    Number of attendances to lectures
    Number of attendances to evaluations

  • Basic plan: write up educational intervention for critical care/anesthesia fellows (daily question emailed to all fellows). Compare pre- and post-intervention board exam scores, overall and by subscores. Suggested basic descriptive statistics for pre- and post-intervention groups (N=7 fellows in current data, with another 14 fellows undergoing intervention now), stripcharts and Wilcoxon test to compare pre- and post-intervention scores.

2014 October 1

Craig Sheedy, Emergency Medicine

  • This is a follow-up visit to the one made September 10. Craig is working with Don Arnold in Pediatric Emergency Medicine as his mentor on a study looking at whether passive oxygenation positively influences O2 sat levels during intubation. They have retrospective data on 44 subjects who were intubated without passive oxygenation. Because this practice is now standard of care at Vanderbilt, a randomized trial cannot be used to study their question of interest.
  • Retrospective data is limited to 44 patients, with main outcome of interest = lowest O2 sat during time it takes to intubate. Outcome is not normally distributed, so for actual analysis, something like Wilcoxon test is more appropriate, but using PS's t-test calculations and retrospective data, we roughly estimate 80% power to detect a difference of 15% saturation with 1:1 ratio (new vs retrospective data), and about 13% with 2:1 ratio.
  • Using lowest saturation during intubation attempt as the outcome, adjusting for age and possibly race and gender, we estimate about 40 hours for analysis.

2014 September 24

Justin Godown, Pediatric Cardiology

  • Email: I have an analysis to be performed involving a multivariable logistic regression looking at risk factors for antibody development after pediatric heart transplantation. I was planning to apply for a VICTR voucher for this project. Do I need to attend a clinic to discuss this prior to applying? How many hours do you usually estimate for a project like this? It should be fairly straightforward.
  • Suggest limiting followup to first five years after transplant, limiting patient population to those transplanted >=5 years ago; this deals with issue of very different followup times (patients in database transplanted from 1987 - 2014). Possible secondary analysis looking at development of antibodies by one year after followup.
  • Covariates include ischemic time, pre-transplant antibody presence, etc. Anticipate ~50 events, so can include only 5 parameters in model.
  • Goal is abstract with a possible manuscript; Justin says data is very clean (stored in REDCap). Estimate 40 hours for manuscript.

2014 September 17

Travis Ladner and Eric Wise, Surgery

  • Genetic research in cardiovascular disease using BioVU
  • 100 patients, 20 vasospasms (on initial screen), 40 possible SNPs; thinking of ~40 logistic regression models (vasospasm = SNP + covariates)
  • recommend either Tuesday biostat clinic or VANGUARD clinic in PRB

Justin Godown, Pediatric Cardiology

  • The project is looking at strain (a measurement by echocardiogram) in patients after heart transplant. We want to compare the measurement to a group of normal controls at different time points after transplant. We also want to see how this measurement changes in the setting of rejection or coronary disease.
  • Transplant patients get echos twice a week right after transplant, spaced out to every three months or so eventually
  • Hypothesis is that rejection and coronary disease might be able to be detected earlier via a change in strain measured on echocardiogram
  • "Normals" will be patients who are referred to clinic for murmurs, etc, but have normal echocardiogram; will be matched by age and gender at each time point
  • Question 1: compare strain values in transplant patients to "normals" at transplant, 1 month, 1/3/5 years after transplant; probably estimate 40 hours for this portion
  • Question 2: predict rejection by previous echocardiogram values, time between previous echo and rejection, and interaction term between them for effect modification (logistic regression with repeated measures - use Huber-White sandwich estimation using patient as cluster); estimate about 100 hours for this portion
  • Check with Frank Harrell, Yanna Song and/or Chris Slaughter for possible collaboration plan; otherwise, plans to apply for VICTR
  • Strongly suggest using REDCap for data collection, since it will make data management/cleaning/analysis much easier

2014 September 10

Craig Sheedy, Emergency Medicine

  • "We are trying to start a project in the pediatrics ED and would like to calculate sample sizes needed for the study."
  • Craig is working with Don Arnold in Pediatric Emergency Medicine as his mentor on a study looking at whether passive oxygenation positively influences O2 sat levels during intubation. They have retrospective data on 45 subjects who were intubated without passive oxygenation. Because this practice is now standard of care at Vanderbilt, a randomized trial cannot be used to study their question of interest.
  • We used PS to look at various sample sizes and different ways of approaching the study to determine whether the study will have sufficient power to detect a difference based on the number of subjects that will be feasible to recruit within a year.
  • Craig will discuss with his mentor and both will return to clinic for further evaluation after discussing the results we saw today.

2014 September 3

Silky Chotai, Vanderbilt Spine Center

  • I am a research fellow at the Vanderbilt Spine center, we are working on a grant proposal. I have some questions regarding the biostatistics.
  • Primary aim is predictors of patient-centered outcomes, specifically pain (continuous score from validated scale). However, different surgery types get different pain scales, so cannot include all surgery types in the same model for pain (six types of pain scores). (EQ5D, a QOL score, is used across surgery types.)
  • Secondary aim is to use surgical patients in the spine registry to look for predictors of high direct/indirect/total costs for patients with various surgery types. Start by looking at distribution of cost outcomes/residuals - linear regression might be appropriate, but need to look at distribution to see whether transformation or other model type is necessary.
  • Tertiary outcome: identifying cost "outliers" - factors which determine patients who have unusually high costs.
  • Some patients are included multiple times due to revision surgeries, and all are followed up for pain scores at multiple time points, so repeated measures are important.
  • Strongly advise against using univariate analyses to select potential predictors; this will lead to potentially misleading and/or nonreproducible results. Instead, use clinical knowledge to select potential risk factors and interactions/effect modifications to include in models.
  • Plan is to apply for VICTR voucher.
  • For future projects with smaller sample sizes, discussed data reduction techniques like propensity scores, prioritizing degrees of freedom, etc.
  • Mentor is unable to come on Wednesdays due to OR schedule, so recommend returning on a different day and having him available for discussion.
  • Possibly split into two separate projects, since all the above would easily run >200 hours. Planning to use REDCap for data collection.

2014 August 27

L. Tyson Heller, Jennifer Green, Dr. Rice, not present, Med/Peds

  • Under the umbrella of improving IV access on the general medicine floors in general, we have a proposal for a simultaneous study on the placement of ultrasound-guided peripheral IVs placed by Medicine Housestaff.
  • Design for testing intraosseous catheters against central lines. the catheters can be put in much more quickly than the central lines
  • Discussed whether/how to randomize in previous clinic
  • For patients who have codes, there is about 5-10%
  • Increase in return of spontaneous circulation (y/n)... event note is sent to redcap
  • cerebral
  • Questions about designing a study.
    • What to collect
  • How is time off of iv access measured?
  • other study: would training in ultrasound _ decrease the time off
  • Clinically, being without iv access for more than 4 hours is unacceptable.
  • They are trying to decrease the time between being without access until IV therapy places the IV.
  • Timepoints: time of loosing access, IV consult request, time IV placed by IV therapy.
  • Could there be additional variablility due to the prioritizing for urgent patients.

Douglas Conway, Vanderbilt Institute for Clinical and Translational Research

  • I would like to attend the Wednesday, 8/27/14 biostats clinic regarding an upcoming RCT. We need some power calculations done to determine the study size needed. We have collected pilot data from around ~70 individuals through a survey. One of the outcomes of the study will hopefully be a significant change in quality of life (QoL), measured by a 29 item instrument/questionnaire taken at the beginning of the trial and several more times throughout. We want to know how many people we will need to enroll to hopefully show significant, powered stats of change. The 29 item instrument is within a larger set of questions that makes up our pilot data. That raw data has been attached as well as a scoring guide generated by the institution that created the instrument. I look forward to meeting with you all tomorrow.
 

2014 August 20

Ahilan Sivaganesan, MD - Neurosurgery

  • Needs help with database design, data cleaning, and possible extraction of data from StarPanel /Wiz.
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2014 August 20

Ahilan Sivaganesan, MD - Neurosurgery

  • Needs help with database design, data cleaning, and possible extraction of data from StarPanel /Wiz.

Ashly Westrick, MPH - Neurosurgery

  • She is working on a VICTR application for funds for biostat support for a retrospective abusive head trauma study, and I'll need to include in my application the proposed length of time for analysis (cost, etc).
  • Wants to describe the population. Model with disposition as the outcome, i.e. home with mother/father, home with other family, rehab. Model with death as the outcome (has about 25 deaths).
  • Approximate 60 hours for analysis and manuscript.

2014 July 30

Luke Krispinsky, Pediatric Critical Care Fellow

  • Needs an estimate of time needed for VICTR application.
  • Main outcome is endothelial function post-bypass vs exposures including age, weight, baseline endothelial function, potentially sedation (though sedation is complex because different drugs are used)
  • Secondary analyses: correlate endothelial function with additional outcomes (peak lactate, fluid, vasoactive ionotrope score) and correlations between endothelial function and biochemical qualities
  • Eventually look at mortality, time on MV, time to ICU and hospital discharge, but not in the scope of the first manuscript
  • Check into potential collaboration with cardiothoracic surgery (Frank or Hui?); if VICTR voucher is submitted, estimate max of 100 hours for above

Malena Outhay, SOM

  • Gave intervention in trauma education to both medical personnel and laypeople in Mozambique, along with pre- and post-intervention tests; currently have 88 subjects, roughly 50/50 medical vs laypeople
  • Main question: are pre- and post-intervention test scores different, and does that difference depend on medical vs layperson (may be other potential confounders as well, but incomplete data on these)
  • Test scores could be 0-100; actual data ranges from about 13-90% and is pretty normally distributed
  • Potential collaboration with IGH - check with Meridith to see if this project is covered
  • Recommend paired t-test for first question, then linear regression: post-test = pre-test + group, or possibly post-test = pre-test * group (interaction + main effects) if hypothesis is that regression slopes will be different for medical personnel vs laypeople
  • Adjusting for additional confounders difficult due to incomplete data

2014 July 23

Calvin Gruss, Department of Anesthesiology

  • Calvin is asking for help in analyzing data from an anesthesiology project comparing the accuracy of a neck circumference estimation with a true neck circumference.
  • Two main questions: is "gold standard" neck circumference measurement repeatable, and is a new method (via digital photo) as reliable as gold standard?
  • Recommend Bland-Altman test for both, but not in Excel add-on package; look for resources like SPSS (possibly contact Jonathan Schildcrout or Matt Shotwell for direction, since falls under anesthesia collaboration)
  • Possible reference

Austin Adams, ENT Surgery

  • Follow-up questions from last week's clinic. Helped with PS calculations.

2014 July 16

Kelvin Moses, Urologic Surgery

  • wants preliminary results for a grant submission.
  • requesting data from Southern Community Cohort Study (SCCS). Needs power analysis and statistical plan for the data request.
  • applying for VICTR biostats support for funding for this prelim project. Needs estimate.

Alex Seelochan, Anesthesia

  • Original email: My name is Alex Seelochan, and I am currently affiliated with the summer anesthesia program at Vanderbilt. I wanted to request whether I may come to the Wednesday class of the Bio statistics Lab to revise Fisher's Exact Test. Specifically, I do have data to consider. I have attached the following table for your reference. My mentor (Dr. Thomas Austin) and I are trying to do the appropriate analysis and extrapolate patient sample needed for significance. Moreover, I have been using PS.
  • There are two groups, intubated at 20 and 30 centimeters of water; main question is whether post-intubation pressures are different between the two groups. Original plan was to collapse into "in acceptable range" vs. not; we instead recommend keeping these values continuous, graphing data (boxplot + stripchart), and doing a t-test or, more likely, a Wilcoxon rank sum test to compare the two groups.
  • Recommend using SPSS for graphs and analysis rather than Excel.
  • PS can calculate number of patients needed in each treatment group to see a difference of __ assuming one exists.

Austin Adams, ENT Surgery

  • Planning to apply for VICTR voucher - will eventually need estimate of hours
  • Plans prospective RCT comparing two types of intubation, with several outcomes of interest; will collect data via REDCap
  • Will use PS to calculate sample size on primary outcomes: aspiration (yes/no) and patient satisfaction postop; need pilot data/estimates for both quantities (eg, what % do we expect to aspirate in each group, or how satisfied are patients under usual care and how much of a difference would be meaningful - need measures of variability, like standard deviation, in addition to estimates)
  • Also need to figure out how to measure patient satisfaction - visual analog scale, Likert scale, simple satisfied vs. not satisfied? This will affect power calculations
  • Use REDCap to full advantage - take advantage of numeric fields/ranges, dropdowns, etc (this will maximize stats support by minimizing data cleaning time)
  • Talk to Matt Shotwell and Jonathan Schildcrout (PhD biostatisticians) about possible collaboration plan with anesthesiology

2014 July 9

Catherine Bulka, Anesthesiology

  • General question: propofol dosing required for loss of consciousness has been shown to differ by race, but providers are often not considering race in choosing doses; wondering whether this will be improved by educational intervention for providers
  • Concern is that studies which suggest difference between races may not be strong and/or generalizable
  • Also, given VUMC patient population, unlikely that we'd be able to see any differences between races other than white vs. African-American
  • Alternate research question: assuming everyone starts at same loading dose (by weight), do different races require different maintenance doses? Recommend mixed effects approach to account for differences among providers.

Anji Wall, General Surgery/Bioethics

  • Original email: "I am a general surgery resident, with a PhD in bioethics, and am planning to start a project assessing the common ethical issues discussed in MMI conferences. I have a paper survey tool and a coding guide, which I am planning to use for data collection... I would like assistance with determining sample size, format for data collection and the type of analysis to conduct. I do not have research funding or a formal research mentor but will attempt to get funding through VICTR if this is something that you all think would be warranted."
  • Recommend collecting data in REDCap - no need for numeric vs. character coding, etc
  • Main goal is to determine how best to educate surgeons on clinical ethics topics; main question: are ethical issues discussed equally often in morbidity vs mortality cases?
  • For descriptive purposes, plan to start with 100 cases, then use that as pilot data or proceed with morbidity vs mortality comparisons from there

2014 June 18

Jennifer Morse and Emmanuel Okenye, Perioperative Clinical Research Institute

* "I am assisting one of our summer students with a research study. We are planning on attending the Wednesday clinic together to get some advice on performing an analysis.

The investigator is trying to determine if there is a statistical difference between the time to successful intubation between 2 devices when used on mannequins. There are two sites (Us and San Antonio). At each site, there were 5 anesthetists who each performed 6 trials with each device.

In my initial analysis, it was determined that the total time to success was not normally distributed. The two devices appear to result in different lengths of time but I am unsure what test to use. Mann-U? Can that account for the multiple trials per individual? In addition, there was a large difference between the two sites due to a different mannequin and different experience levels of the anesthetists (This demographic data was not captured).

* Data is skewed, but not terribly, so suggest a linear model with sandwich estimation to adjust for within-subject correlation (checked model diagnostics in clinic). Example code:
library(rms)

## Model without interaction, since interaction may be underpowered with 10 subjects
mod1.ols <- ols(Total.time ~ Site + device, data = mydata, x = TRUE, y = TRUE)
## fit original model, without accounting for within-subject correlation
mod1.robcov <- robcov(mod1.ols, cluster = mydata$Subject)
## use Huber-White sandwich estimation to account for within-subject correlation
mod1.robcov ## get coefficients, p-values

dd <- datadist(mydata); options(datadist = 'dd') ## needed to get predicted values
plot(Predict(mod1.robcov))
## Plot predicted times for each site, device held at mode of other variable (eg, predicted times for Vanderbilt and SA held at most frequently tested device)

## Calculate predicted values for all combinations of site, device, save as data set to use in additional plots
pred.data <- Predict(mod1.robcov, Site = c('Vanderbilt', 'San Antonio'), device = c('LMA', 'i-gel'))

## Repeat above for interaction model as sensitivity analysis, replacing "+" with "*" in ols() call
## Repeat also for time for first step in process (similar outcome distribution)

2014 June 11

Stuart Ross, Anesthesia

  • "I'm in the beginning stages of a project with the anesthesia department and I wanted to get some ideas about how to best collect information. Later I'll be getting data from charts here at Vanderbilt, but the goal is to compare patients here with those elsewhere. The gist of it is comparing how patients from various contries/ regions differ from those here. What I want to do is take information from however many sources I find and organize it in a way that makes it easy to use/ search/ etc etc. Maybe a simple excel spreadsheet will do, but I wanted to make sure there wasn't an easy way of doing this that I might not be aware of."
 

2014 June 4

Tom O'Lynnger, Neurosurgery

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  • Iím interested in attending the Wednesday biostats clinic to discuss a project I am conducting about outcomes in pediatric traumatic brain injury after ICU protocol implementation. The main analysis is an ordered logistic regression involving Glasgow Outcome Scale and a second ordered logistic regression involving discharge disposition. Iíd also like to predict favorable discharge disposition using logistic regression. I have 129 total patients and have already done an analysis myself (Iím an MPH student in addition to being a resident in neurosurgery) that I believe is accurate but would like to confer with an expert. If possible itíd be great to go over the analysis during the session, though if not, Iíd plan on getting VICTR support.
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  • "Iím interested in attending the Wednesday biostats clinic to discuss a project I am conducting about outcomes in pediatric traumatic brain injury after ICU protocol implementation. The main analysis is an ordered logistic regression involving Glasgow Outcome Scale and a second ordered logistic regression involving discharge disposition. Iíd also like to predict favorable discharge disposition using logistic regression. I have 129 total patients and have already done an analysis myself (Iím an MPH student in addition to being a resident in neurosurgery) that I believe is accurate but would like to confer with an expert. If possible itíd be great to go over the analysis during the session, though if not, Iíd plan on getting VICTR support."
  • We went over Tom's analysis and made some suggestions, including: describing continuous variables with medians and IQRs instead of means and SDs; doing Wilcoxon tests rather than t-tests for descriptive statistics/table 1; removing sex and race from the model to avoid overfitting; combining the single patient discharged to acute care with the patients discharged to rehab; making sure that Stata is coding the outcome variable as expected; producing a boxplot of raw data for before/after and favorable/unfavorable discharge disposition vs GCS.
 

2014 May 21

Heidi Smith and Natalie Jacobowski, Psychiatry and Anesthesiology

  • Study to describe pediatric delirium in ICU.
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2014 June 4

Tom O'Lynnger, Neurosurgery

  • Iím interested in attending the Wednesday biostats clinic to discuss a project I am conducting about outcomes in pediatric traumatic brain injury after ICU protocol implementation. The main analysis is an ordered logistic regression involving Glasgow Outcome Scale and a second ordered logistic regression involving discharge disposition. Iíd also like to predict favorable discharge disposition using logistic regression. I have 129 total patients and have already done an analysis myself (Iím an MPH student in addition to being a resident in neurosurgery) that I believe is accurate but would like to confer with an expert. If possible itíd be great to go over the analysis during the session, though if not, Iíd plan on getting VICTR support.

2014 May 21

Heidi Smith and Natalie Jacobowski, Psychiatry and Anesthesiology

  • Study to describe pediatric delirium in ICU.
  • Want to study relationship between diagnoses of delirium and physicians' use of certain descriptors.
  • Intensivists' description of patients who were diagnosed as having delirium.
  • They have developed a list of areas: agitation,
  • One factor is whether delirium is mentioned in the problem list and in the plan. There is a daily physician note.
  • Some medications could contribute to delirium. This could be reflected in nurse notes or medication record.
  • They have three observation times for each patient. The day prior, the day of, and the day after the day delirium was diagnosed.
  • Discussed need for comparison with patients who were not diagnosed with delirium for the inferences they are interested in.
  • Could select controls based on matching on important patient factors.
  • It is important to consider which day's observation to use for patients who didn't have a diagnosis of delirium. One option is to consider the
  • You could also potentially consider including all observations for the patients.
  • They are considering using VICTR for biostatistical support.
  • We think Jennifer Thompson would be well suited for this project and should give the time estimate.

Ben Mackowiak, Neonatology

  • Acidosis and pulmonary hypertension in neonates.
  • Has experiments on piglets whose pulmonary vessels were exposed to acid in three doses until a certain pH is reached.
  • They have a machine that reads the percent dilation
  • Discussed problems with analysis on percent change. An alternate way to control for the initial size is to use a regression model controlling for the initial size.
  • You can estimate the (absolute) mean difference between the initial and final data. You could do a paired t-test between the baseline and result after the first application of acid.
  • A good approach would be a mixed effects regression model with a fixed effect for dose and a random intercept for subject (pig vessels).
  • Should plot the trajectories and see how linear they are.

2014 May 7

Shreyas Joshi, Urology

  • Appling for a VICTR grant and would appreciate assistance powering our study and determining the most appropriate data analysis plan for the study.
  • Overall, 49 patients died.
  • Looking to correlate preoperative sarcopenia with postoperative outcomes in patients undergoing surgery for Renal Cell Carcinoma (RCC).
  • We are using a program that determines the skeletal muscle index on preoperative CT scans to obtain our "preoperative sarcopenia index" variable. Sarcopenia is lack of muscle mass. It is a newer measure of nutrition.
  • It may be nice to have some or all of the scans re-scored to see how reliable it is. You can look at the agreement in the scores. Or, if there is already a study published,
  • Have 250 pre-op ct scans. All patients who get the surgery should have the scan. Whether they have the scans would maybe depend on the referral patterns.
  • So far, we have overall and disease-specific survival data, and we are working on gathering 30/90 day complication rates and hospital-free-days.
  • We hope to be able to power the study for survival (overall or disease-specific) in order to move forward with data analysis.
  • They are applying for biostatistics support, and we estimate that the project will require 50 hours of statistician work for this project and manuscript.

2014 April 30

Calvin Gruss, Anesthesiology

  • studying the effects of acute hypoxia longitudinally in ~100 healthy subjects. Study has three phases, subjects had hypoxia first, then had carboxyhemoglobin/methemoglobin, at last they had hypoxia+elevated carboxy/methemoglobin. Each subject had two runs. The # of measurements for each subjects is from 20-26. Interested in assessing the relationship between % carboxyhemoglobin and hemoglobin concentration in the blood.
  • suggest using mixed effect model. Additional stat help can be get from Dr.Schildcrout or Dr.Shotwell or VICTR biostat support.

2014 April 2

Christy Goben, PICU

  • Needs quote for VICTR biostat support; study in pediatric critical care with a focus on sedation trends in the PICU over the last decade with delirium impact
  • No delirium screening prior to 2008-2009 (PCAM introduced); now done on children 5yo+
  • Delirium re-education done in 2011, so plan to compare three time periods (no screening, post-screening education, post-re-education)
  • Expect overall use of sedation to decrease over time with possible exception of Precedex/dexmedetomidine
  • Pulling data from StarPanel, ICU only (not after transfer to floor); suggest collecting in longitudinal format rather than summary if feasible
  • Likely to have multiple ICU stays for some patients; will have identifier, so can control for within-patient correlation
  • Plan to describe delirium prevalence, use/dose of several sedatives and antipsychotics (primary goal), and secondarily, correlate/model these vs outcomes (ICU LOS, hospital LOS, time on vent, mortality)
  • For outcomes, need to collect potential confounders as feasible (eg, use of pressors, sepsis, diagnosis/procedure codes, SOI, etc - whatever seems reasonable)
  • Suggest very preliminary estimate of 120 hours, pending discussion on informatics/pulling data

2014 March 26

Luke Krispinsky, PICU

  • Pediatric critical care fellow looking at endothelial dysfunction before and after cardiopulmonary bypass in infants (0-12m) undergoing repairs of congenital heart defects using iontopheresis and monitor that can quantify distal perfusion made by Perimed
  • Since this is a new machine, suggest taking multiple measurements on same patient/same time to gauge reproducibility - may be restricted by cost ($13/probe)
  • Mainly interested in a) describing change in endothelial function b) seeing how change is associated with outcomes (eg, ICU LOS)
  • Exposures: difference between baseline and lowest (post-surgery) measurement, difference between post-surgery and 24h measurement, and AUC
  • Outcomes: ICU (average 3-14 days, depending on type of surgery and other variables) and hospital LOS (varies widely), ianotrope score (need for BP meds), fluid requirements, vent LOS, mortality (<10%) - within defined study period, like 30 days?
  • Next steps: define exposure(s) and outcomes(s) of primary interest, get estimates of variability on those outcomes from literature, think about potential confounders like severity of illness

2014 March 12

Christy Goben, PICU

  • Needs quote for VICTR biostat support.
  • Study in pediatric critical care with a focus on sedation trends in the PICU over the last decade with delirium impact
  • RESCHEDULED

Sarah Scott, MD candidate, Director of Pharmacy Shade Tree Clinic

  • Needs a quote for VICTR biostat support.
  • Small study using a cohort of pediatric critical care patients. Her topic is the association of acute kidney injury and mortality in children on ECMO.
  • RESCHEDULED

Angela Maxwell-Horn, Developmental Medicine

  • Briefly, I am doing a training on developmental screening for
pediatric residents when they rotate through my department (developmental medicine). I am going to examine the well-child checks that they do in their continuity clinic both before and after the training to see if their practice changes in how they screen and refer. Another aspect is that I am also contacting their preceptors in the clinic to see if they think the resident does a better job of screening after the training. Currently, there are 26 pediatric interns. I want to know how many patient charts I need to look at before and after the training to make any results significant. Additionally, we are thinking of breaking up the next intern class into two groups...one group would get the in person training by me, and the other group would watch a video recording online. I am concerned, however, that this will considerably lower the power of my study.
  • Recommended: sampling all relevant charts for each intern from month prior to and month after training; maybe stratified chi square test for # charts with appropriate screening. First step: how many interns are currently doing appropriate screening?
  • Angela will talk to preceptor and finalize outcome variable, get initial idea of how many people are doing screening correctly pre-training.

2014 March 5

Kendell Sowards, Instructor in Surgery

  • Has requested feedback on a power calculation.

Eileen Duggan, Pediatric Surgery

  • Questions regarding how to treat missing race and insurance data in large dataset; how to build best model for overall adverse event binary outcome (specifically how to adjust for hospital clustering, how to treat race and insurance variables (i.variable or different variables for each race/insurance status), and choosing the best model); and working with an interaction term in this model.
  • We recommended adjusting for hospital using a random effect in her logistic regression model and that the best way to build her model was through pre-specifying predictors to include based on literature and clinical knowledge.
  • Her data has missing data at random so recommended that she impute data to avoid biased estimates.
  • We also discussed how best to deal with and present terms with interactions -- always report together, never just report a main effect.
  • Finally, we discussed different ways of coding categorical variables such as race. She had seen in the literature that sometimes it is recorded as a multi-level single variable and other times it is recorded as separate indicator variables. Much depends on her question and discussed the different interpretations of the different ways of coding the variables.

2014 February 19

Aaron Benson, Urology, mentor/PI Nicole Miller

  • multivariable logistic regression predicting sepsis
  • an important independent variable (preoperative nephrostomy tube) is omitted(?) because there are no observations of the dependent variable (sepsis) in patients with the preoperative nephrostomy tube (n = 67) and 9 observations of sepsis in patients without a preoperative nephrostomy tube (n = 152). For this test, the results state that the preoperative nephrostomy tube "predicts failure perfectly". My questions are: how might I explain this to reviewers of our manuscript and is there another test that I should use?
  • Having a nephrostomy tube shouldn't have impacted the length of times.
  • Addressed whether there is a time after which everyone gets nephrostomy. The use has increased. This would be something to discuss in the discussion section.
Here are some follow-up questions I sent Dr. Benson, along with his answers:
What is the purpose of the model? Is it to make predictions for patients based on their characteristics? Or to identify the important predictors of sepsis?
The study is a retrospective analysis of our percutaneous nephrolithotomy (PCNL) experience. Most of these patients have access to the kidney obtained as part of the PCNL (i.e., no pre-existing nephrostomy tube). Other patients have a pre-existing nephrostomy tube placed ahead of time -- either because of their history of recurrent UTI, pyelonephritis, high infection risk features, etc. or because they presented acutely and had the nephrostomy placed at that time. We are basically trying to determine whether patients with a nephrostomy tube prior to PCNL are less likely to develop post-PCNL sepsis. We are not necessarily trying to identify predictors of post-PCNL sepsis (already lots of data), but rather whether pre-PCNL nephrostomy tube (with renal urine culture and specific antibiotics) may be protective against post-PCNL sepsis. After two manuscript reviews, our journal reviewers are recommending multivariate analysis to make sure that the differences in sepsis rates is not due to other factors.
When you say the variable (nephrostomy tube) is omitted, do you mean that your group decided to exclude the variable (nephrostomy tube) from the model? If so, is that because of the result you are getting?
No, I mean that STATA itself is showing the word "omitted" in the row for PCN (nephrostomy tube) where the data should be. We are not omitting the data on PCN because that's the focus of the study.
I'm unsure of what you mean by "For this test, the results state that the preoperative nephrostomy tube "predicts failure perfectly"." Which test? Is it part of the automatic regression output? And where is the "predicts failure perfectly" coming from? The output in stata?
By "this test", I mean logistic regression. The phrase PCN "predicts failure perfectly" is from the STATA output just above/below the results table it produces.
When you say "no observations" of sepsis in patients with the tube, you mean all of the patients with the tube were known to not have sepsis, right? You don't mean whether they had sepsis is unknown/missing?
Correct, in the group of patients who had a nephrostomy tube prior to PCNL (n = 67), there were no sepsis cases. In the group the did not have a nephrostomy tube prior to PCNL (n=152), there were 9 sepsis cases. There is not any unknown/missing data for whether patients developed post-PCNL sepsis.
How many other variables did you have in the model, and how many were considered?
Unfortunately, I left my jumpdrive at home today or I would have already sent you the STATA file. But, off the top of my head, there are probably 10-12 other variables.
  • We recommended using exact logistic regression. In stata you would use exlogistic.
  • Here is a web page explaining this issue and why you need exact logistic regression: http://www.ats.ucla.edu/stat/stata/dae/exlogit.htm. You should be able to try this (you don't need the [fw=] option) in stata and use this to explain your analysis in the methods.
  • We also discussed that the model would be really overfit using 10 variables with only 9 events. We think one variable would be appropriate, but it would also be okay to use 2 variables, maybe nephr. tube and operating time.
Here is some R code:
counts <- matrix(c(143, 67, 9, 0), 
   nrow = 2,
   byrow = TRUE,
   dimnames = list(c("No sepsis", "Sepsis"), 
      c("No tube", "Tube")))
prop.test(counts)
binconf(x = 0, n = 67, method = "all")

2014 February 12

Imani Brown, IGH, MPH candidate

  • Looking at preliminary data from an intervention in HIV+ people in Mozambique. She is interested in assessing what factors are associated with receipt of 9 different messages included in the intervention.
  • Each message is defined as having been 'received' or 'not received' so we suggested logistic regression. We also recommended ranking the predictors of interest so that depending on how many events she has, she can fit a model with the proper number of parameters based on the 10:1 or 20:1 ratio.
  • For those messages with very few events, we recommended descriptive tables and graphs as opposed to tests of association or models.
 

2014 January 22

Stephen Humble, Mayur Patel and Patrick Norris, Trauma

  • Planning to perform non-inferiority power calculations applied to paired observations of heart rate variability.
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Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

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Notes for Wednesday Biostatistics Clinic

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2014 January 22

Stephen Humble, Mayur Patel and Patrick Norris, Trauma

  • Planning to perform non-inferiority power calculations applied to paired observations of heart rate variability.
  • Want to describe heart rate variables to hopefully determine a norm for ICU population
  • Suggest spaghetti plots to describe data for now

Heather Kistka, Neurological Surgery

  • Planning to submit VICTR voucher
  • Compared VUMC residency applications in 2007 (N = 148) vs 2012 (N = 191) to determine whether "misrepresentation" has increased among applicants based on Pubmed searches for publications
  • Problems: 1) application changed in that window; 2) various kinds of misrepresentation (existence, author order, peer reviewed vs not); 3) if misrepresentation had multiple types (eg, not peer reviewed and changed author order), only "worst" was recorded
  • Analyses planned: lots of descriptives by year (number/types of misrepresentation, demographics, etc), plus logistic regression model among 2012 applicants looking at risk factors misrepresentation vs no misrepresentation (ie, "red flags" indicating that application should be closely investigated)
  • Logistic model: 8-9 df (AOA membership, grad degree, board scores [nonlinear?], gender, top 20/non-top 20/foreign med school, # works on CV), works with 84 events in 2012
  • Suggest secondary analysis using a "scale of misrepresentation" as outcome (flagrant vs something getting put in the wrong section) in proportional odds model - lots of ways to look at this (done only on applicants with misrepresentation, looking at "badness" of misrepresentation, or look at worst misrepresentation per applicant...)
  • For above, possibly create weighted score, along lines of DNE*3 + FAO*2 + (MAO + NPR + OPO)*1, for outcome per applicant; if distribution is wacky (probably will be), perhaps take out applicants who didn't misrepresent anything and reduce number of covariates to account for lower N
  • Clinic estimate is 40 hours without secondary analysis, 60 hours with secondary analysis

2014 January 8

Catherine Bulka, Anesthesiology

  • Working on a project analyzing geographic variation in hospital billing practices. I have a dataset of hospitals and what they charge for certain orthopedic surgeries. I have aggregated the hospitals to the core-based statistical area level (these are geographic areas designated by the Office of Management and Budget that are based around an urban center of at least 10,000 people and any adjacent areas that are socioeconomically tied to the urban center by commuting). Rather than look at differences in hospital billing practices nationwide since there are so many confounders, I decided to aggregate the data and look at the amount of variation in billing practices within each core-based statistical area because Iím assuming that the socioeconomics/cost of living/overall health of the patient population/any other potential confounders are likely pretty similar within these areas.
  • Iíve calculated the means and standard deviations in what the hospitals in each area bill for the same procedures, but Iím not sure what the best way is to compare these. The data are not normally distributed, so Iím not sure that standard deviation is even the best way to represent variations in the amount billed. Further, some areas have many more hospitals than others Ė from 2 in one area to 105 in another, which I think should be taken into account. I thought about using ANOVA, but Iím not so much interested in the mean amount billed by the hospitals in each area, since certain areas of the country (California, NYC, Florida) are known to charge more for certain procedures than other areas for economic reasons.
  • How can I best compare the amount of dispersion between many groups (there are > 500 areas that Iíd like to compare), while addressing differences in sample size? * Have calculated the coefficient of variation (standard deviation/mean * 100) for each core based statistical area, although I am not sure if that's the best metric to show variation. Also not sure how to compare these areas with hypothesis testing. * After discussing her project, we suggested she explore a linear mixed effect model as well as further explore some of the geographic representations she had started with somehow including some aspect of the variation of charges by region in addition to reporting mean charges by hospital within a region.

Michael DeLisi, Biomedical Engineering

* Michael has a project comparing how his intervention to image-guided surgery for minimally invasive eye surgery helps in time to reaching the desired target and the ability to hit the desired target. * The study uses 4 skulls with different targets per eye. In each skull, one eye is operated on using standard image-guided methods; the other eye uses the enhancement to the standard methods. Sixteen surgeons were tested, each operating on each of the skulls. The order of skulls for each surgeon was the same but the order of methods of surgery was randomized. * Currently, he has tested for differences using t-tests and F-tests. * Our recommendation was to use linear and logistic mixed effects models to account for the correlation among surgeon, including method of surgery, skull, and eye (?) as covariates in the model with surgeon as the random effect.

2013 December 11

Catherine Bulka, Anesthesiology

  • Wants to demonstrate/examine group balance wrt baseline characteristics after matching using propensity scores.
  • Calculated propensity scores for receiving regional anesthesia during surgery, as opposed to general anesthesia, based on several factors that anesthesiologists in the department deemed important in their decision making process, such as the age of the patient, duration of the surgical procedure, etc. I then matched patients who received regional anesthesia to those who received general anesthesia by their propensity score.
  • consider calculating the "standardized differences" between the groups and plotting it. Something like this: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472075/figure/fig01/
  • Computing confidence limits for effects are probably in order, as opposed to hypothesis testing. It's not just large samples where hypothesis testing presents interpretation
problems; we generally like interval estimation.
  • In building the propensity score it is appropriate to not be parsimonious (i.e., it is not approriate to remove variables from the model), and to allow effects of continuous variables to be nonlinear.
  • There is controversy over matching vs. covariate adjustment for logit propensity score. Matching usually results in some patients being discarded, which is problematic.
  • research question is to compare hospital length of stay in those who got regional anesthesia vs. regional. They used prop. score matching to match.
  • Wants to demonstrate group balance wrt several variables.
  • Instead of using propensity scores to select the patients, you could take all patients who met inclusion criteria, but use weighted propensity score analysis
  • Catherine did try the standardized differences that Meng suggested, but the problem is the surgical duration variable still looks like it is too different. However, it turns out that the standard deviations she used were from the selected patients, while the paper recommends using the ones from the original group of patients from which the two groups were selected. She will re-evaluate the quantities and reassess whether the groups are balanced.
  • If the groups still don't look like they are comparable, could look at some of the methods Robert Greevy has written for matching in observational studies.

2013 December 4

Jo Ellen Wilson, Psychiatry fellow in psychosomatic medicine

  • Needs AP and quote for VICTR proposal.
  • Not a reliable way of distinguishing delirium from catatonia; plans to assess patients in two ongoing delirium studies for catatonia to see if there is a clinically relevant overlap between definitions
  • Main study personnel will administer main delirium assessment (CAM-ICU - yes, no or unable to assess); Jo Ellen will administer, within 2 hours, delirium subtype screening (hyperactive, hypoactive, mixed or no delirium) and catatonia screening (scoring: first 14 items are administered; if patient meets two criteria, considered to have catatonia and an additional 9 items are done to assess severity)
  • Delirium subtype screening and catatonia screening both look at previous 24 hours, whereas CAM-ICU is immediate, current assessment
  • Technically, patients can't be considered catatonic if delirious
  • Suggest doing entire Busch-Francis assessment even on patients who don't meet catatonia criteria on items 1-14
  • Plan to look at individual assessments (two per day in ICU, one per day in wards), not full days or summary measures
  • Treatments for delirium and catatonia are sometimes completely opposite - e.g., benzodiazepines avoided in delirious patients but recommended for catatonia, and vice versa for antipsychotics
  • Aims 1-2 focusing on in-hospital outcomes (agreement between delirium and catatonia assessments); aim 3 focuses on hospital LOS, long-term outcomes, and might require more complex modeling to account for possible relationship between delirium/catatonia diagnosis and treatment
  • Aims 4 & 5 deal with medications - parent databases collect 24h totals, but Jo Ellen could get exact times and doses from StarPanel later
  • Suggest splitting into two projects: aims 1 & 2 (perhaps 4 & 5, depending on what exactly is needed - Jo Ellen will discuss with mentor Wes Ely), then aim 3 - long-term and clinical outcomes
  • For sample size calculations, will need to think about specific differences hoped for (differences in proportion or test scores, for example); Jennifer will look at parent study enrollment numbers for average # enrolled per month at VUMC
  • For aims 1 & 2, we estimate 50 hours (likely using kappa statistics for delirium vs catatonia agreement and model of catatonia score ~ CAM [yes/no/UTA] with repeated measures for delirium vs catatonia scores); if aims 4 & 5 are added, more time will be needed. Aim 3 will be addressed at a later date.

2013 November 20

Justin Gregg, Chang

  • previously approved study with VICTR funding that may need some additional statistical support ($1100 left from VICTR grant)
  • looking at time to recurrence/progression of non-muscular invasive bladder disease
  • retrospective data collected 2002-2011
  • could possibly fit under collaboration with anesthesiology - suggest talking to anesthesiology department first
  • suggest estimate of 60 hours to account for data management and analysis if VICTR grant is needed

Mayur Patel

  • Prospero-082713.pdf: Mayur's file
  • systematic review. Want to know whether any meta-analytical technique is possible considering it is largely based on pre-post retrospective case-series data (mostly exposure only, partial cohort type studies; full 2x2 rarely available) and where the event rate is nearly always zero.
  • Our current thought is that there is no analysis possible here Ė but wanted to confirm that, as I've read about continuity correction of 0.5 for zero-event studies, but that seems more applicable for non-repeated measure type samples where controls/treatment groups are distinct.
  • http://www.prisma-statement.org/statement.htm
  • He provided more detail on the studies included in the systematic review. Our recommendation was that a meta-analysis would not be appropriate but directed him to prisma-statement.org

2013 October 30

Jackie Shuplock, Pediatric Cardiology

  • Prospective cohort (2007-2013) of cardiac surgery patients. Some received dex, others did not. Interested in examining the association of dex use with cardiac arrhythmias in the post-op period.
  • Needed input on how best to define the model of interest. Currently using a variable selection process based on univariate analyses. Concerned about differing significance with the inclusion/exclusion of particular covariates. Recommended using clinical knowledge and information from literature to guide the selection of covariates.
  • Four anesthesiologists were involved in the surgeries (any need to adjust for anesthesiologist?) and there were a total of 783 arrhythmias recorded.
  • Dex was rarely used 2007-2008. We recommended limiting the scope of data to 2008 - 2013 to avoid any kind of time bias. We also recommended adjusting for year of surgery in the model in case there were any changes in surgical protocol across time.

2013 Rocktober 21

Francheska Desravines, Meharry

From last clinic:
  • study: The risk of early discharge following pediatric cardiac catheterizations in infants and young children.
  • aim is to find relative risk of a minor or major complication occurring after 6hrs post cardiac catheterization procedure.
  • .xlsx file saved as csv on ClinicsData
  • Data are retrospective chart review on kids 0-4 with cardiac cath between July 2007 and 2012
  • Want estimate of proportion of events with confidence interval
  • To identify important factors, can fit a multivariable model with factors such as age, diagnosis (one v. two ventricle), source of pulmonary
blood flow
  • Data on children over 4 would be important.
  • There were 24 patients with major complications that would require major intervention
  • For a 15 kg child, the estimated model-based prob of major complication is 0.026 (95% CI 0.015-0.046). logistic regression with weight
  • Can see Vincent Agboto, who is a statistician at Meharry.
A new dataset is on ClinicsData.
  • Describe the outcome (major complications) against some of the variables graphically, as in boxplots
<> > binconf(x = sum(card$majorYN == "Major complication"), n = nrow(card),
+ method = "wilson") PointEst Lower Upper 0.03259452 0.02217373 0.04767391
  • The (unadjusted) estimated probability of a major complication is 0.03 (95% CI 0.02, 0.05).
  • Recommend a forest plot showing the model-based probability of major complications with CIs for each combination of ventricles and blood source.

Catherine Bulka, Anesthesiology

Questions:
  • Looking to identify risk factors for developing pnemonia.
  • In her poisson regression, she used the binary outcome of whether the patient developed pnemonia
  • Discussed best way to measure outcome.
  • Have been using a Poisson regression model with a log link function and person-time offset. The person-time was calculated as the time the patient was at risk for developing postop pneumonia (i.e. #days between date of surgery and pneumonia onset, death, or hospital discharge). I am now questioning whether another type of model would have been more appropriate. I was trying to avoid survival analyses because in this context, it seemed like rate ratios made more sense as opposed to hazard ratios. I also wanted to account for time at risk in the model, since some patients were lost to follow-up, which is why I avoided logistic regression. Is Poisson regression the appropriate choice for my data?
  • Had been using a backwards elimination approach to select the final model, but there are a lot of issues with using statistical significance to identify which variables to keep in the model. Is there a better method? I have about 20 covariates that have been identified as potential confounders/effect modifiers.
  • A reviewer suggested using bootstrapping for model selection. While I am able to create bootstrapped samples with 1,000 replications and replacement in SAS, Iím not sure how to use these samples for variable selection.
  • We discussed data reduction techniques as an alternative to backward elimination. You could use a propensity score.
  • Cluster analysis.

2013 September 25

Heather Jackson, Anesthesiology, and Kyla Stripling, Neurosurgery

  • Planning small study (30 pts) examining a pain consult prior to surgery: "Does a preoperative pain management consult effect post-operative lumbar surgical outcomes?"
  • Research protocol attached
  • Contact Anesthesiology admin to see whether you have access to the existing collaboration with Department of Biostatistics (Jon Schildcrout and Matt Shottwell)
  • Recommend trying to get a mentor who has some research experience
  • One of the main outcomes is the ODI, a disability index.
  • May need to consider the sample size you would need to have power to show the differences you are interested in showing.
  • To calculate the required sample size, consider the smallest clinically meaningful difference in ODI and also the distribution of the outcome, including standard deviation.

Jun Dai, Epidemiology

  • Jun is responding to reviewer comments to her manuscript.
  • She has a survival model with covariates for the amount of food eaten. There are three types of fruit, and three corresponding variables.
  • The original research question was about the effect of total fruit consumption on survival. However, the reviewers asked for analysis which considered the amount of fruit separately by type of fruit. Then the reviewer wanted a test of whether the effects of the different fruits are the same.
  • We recommended that she respectfully explain that while his questions are interesting, they are beyond the scope of the paper.
  • Additionally, the test the reviewer recommended is a test for interaction, and doesn't address his own question. :[
  • We recommend that she give some descriptive summaries of graphs showing the types of fruits in her data to answer this reviewer's question without deviating from the paper's original focus.

Vaibhav Janve, Institute of Imaging Science

* Hakmook Kang works with imaging data and with Vanderbilt University Institute of Imaging Science, and we recommend he contact him.

2013 September 18

2013 September 11

Scott McLaurin, Rehab Services

  • Does physical/occupational therapy affect hospital length of stay?
  • Stroke patients
  • Patients were assigned a frequency of visits. The patients were assigned to be in the "meet frequency" group by a systematic algorithm. The patients who were in the "experimental" group were ensured to get their recommended therapy visits. The other group got standard of care.
  • Want direction for designing a new study
  • Could store the data in redcap
  • Could think about using a randomized list of group assignments. There is a mechanism for this in redcap.
  • Need to think about factors that would influence length of stay. You would need to collect these quantities and can adjust for these using a multivariable analysis.
  • We think their project might come under Dan Byrne and Hank Dominico's project.
  • Could go to redcap clinic to help ensure you set up the data the best way. Discussed data quality checks you can build into your database. See https://www.mc.vanderbilt.edu/gcm/rate/index.php/course/view/id/0168
  • If you are not covered under Dan's project, and funds are available, can use BCC (Biostatistics Collaboration). Contact Rameela.

Trisha Pasricha, medical student; mentor Clements.

  • study in GI Laproscopic Surgery
  • in the experimental design stage
  • looking at patient scores on the Beck Depression Inventory before bariatric surgery and scores 1 year following surgery to assess any correlation between bariatric surgery and depression.
  • The Beck Depression Inventory is a 21 question survey that results in a numerical score of 0-63, with higher scores indicating more severe depression and lower scores indicating minimal depression.
  • have several questions about the optimal experimental design (debating between 2 different designs in particular) and how to power the project accordingly.
  • Plan to readminister Beck one year after surgery.
  • Population who have pre-measurment is people who have bariatric surgery.
  • Discussed problems with inference based on pre-post designs.
  • Need to consider the best control population to make the inferences that are of interest. Could consider a general patient group that had any surgery or a specific type of surgery, or a group of obese patients that did not have bariatric surgery. Would want to ensure that patients who did not get the surgery are not systematically different from the patients who did have the surgery. Examples of this would include making the control group consist of the people who were ineligible for the surgery because they were unable to follow the pre-treatment regimen.
  • Discussed importance of minimizing bias that could be caused by people not coming to the one-year follow-up visit. This could bias the results towards patients who are already are not too depressed to come to the follow-up visit. Making the follow-up assessment web-based would mitigate this, but you would also want the pre- and post- surgery assessments to use the same modality.
  • Also discussed looking at the correlation between amount of weight lost and depression
  • Group wants to consider whether they should select patients to include in the study based on degree of pre-treatment measurement. The concern is that there might not be enough patients with lower depression. If you include all patients, then you would get the maximum possible number of patients with low depression, and there wouldn't be value in excluding patients with higher depression.
  • Discussed considering confounding factors like social support. These could be controlled for in a multivariable analysis. The degree of complexity of the relationship (and thus the model required) would inform the sample size required.
  • We would need estimates of the standard deviation of the BDI score and its distribution to calculate the number of patients required.
 

2013 August 28

Angela Joshi, resident, VA

  • observational study of patients who undergo coronary angiogram via the radial artery to determine factors that can predispose to radial artery thrombosis following angiography.
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  • Outcome is workup quality:
      None Incomplete   Complete 
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    1. 1991 1248
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    1. 1991 1248
 
  • Plan on using prop odds
  • In this case, I think we can assume exchangeable cov structure.
  • County info: 1867 unique counties in the data, with 9241 patient records. Here is the distribution of number of observations for each county:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  40  43  44  45  46  47  48  49  53  55  58  59  60  64  71 102 139 161 
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671 368 212 149 73 59 49 36 30 24 18 15 15 11 15 10 12 1 9 8 5 5 4 8 1 3 4 1 3 3 5 6 3 1 1 2 1 3 1 1 1 1 1 3 2 3 1 1 1 1 1 1 1 1 1 1
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671 368 212 149 73 59 49 36 30 24 18 15 15 11 15 10 12 1 9 8 5 5 4 8 1 3 4 1 3 3 5 6 3 1 1 2 1 3 1 1 1 1 1 3 2 3 1 1 1 1 1 1 1 1 1 1
  So there were 671 counties that had only one record in the data, 368 that had 2 obs, and the county that had the most representation in the data had 161 observations.
  • Could use GEE or a random intercept for county to account for practice variation by location.
  • Let m be the number of observations per cluster (subject is the cluster in many longitudinal studies), and n is the number of clusters (subjects). So m is the number of obs per county, and n is the number of counties.
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      • correlation of covariates within subjects: when the covars are between-subject (do not vary within a person (county here)), negligible efficiency loss. Actually, would need to check this.
      • cluster sizes: we have small m (number in each cluster), which should mean small loss of efficiency (see slide 47 of mod 4)
    • If we want to do gee with non-independence weighting, could use geeglm or geese (see slide 87 of mod 4). Can use rob cov in Frank's rms package. This is GEE with independence weight matrix / working covariance.
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    • Gee requires large sample, but we have that. (n>>m Number of different counties is much bigger than the number of obs per county)
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    • Gee requires large sample, but we have that. (n>>m Number of different counties is much bigger than the number of obs per county)
 
    • GEE requires MCAR.
    • Disadvantage of using gee would be that you can't use LRT since you are not specifying the full likelihood. Can use wald tests.
    • Performance with sparse clusters? Is this separate from efficiency issues? In this case, we do have sparse clusters and many clusters of size 1. The issue is variability in the cluster sizes. Jon thinks this is a validity issue.
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    • With glmm, since it's a conditional model with a non-identity link, we cannot get population-level contrasts. The contrasts are subject-specific, and I believe they approximate the pop-level values.
    • The marginal parameters are smaller in magnitude than the conditional parameters. Can think of the conditional params as "inflated" marginal params?
    • There is actually a formula for the relationship between the conditional and marginal parameters in terms of the variance of the random effect (Zeger 1988).
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    • Example of interpretation of subject-specific effects "exp β represents the ratio of the expected odds of respiratory infection for an individual with vitamin A deficiency to that same individual (with same covariate values but) replete with vitamin A"
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    • Example of interpretation of subject-specific effects "exp β represents the ratio of the expected odds of respiratory infection for an individual with vitamin A deficiency to that same individual (with same covariate values but) replete with vitamin A"
 
  • Can check PO assumption using chi square lrt test (difference in deviance) between the prop odds model and the cumulative logits model.

2013 July 31, August 7 and 14

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2013 July 17

David Osborn, Urology

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Jeremy S. Pollock, Internal Medicine, Wes Ely, Mentor

  • Has VICTR biostats request
  • aim of this retrospective study is to determine the incidence and risk factors of delirium in survivors of cardiac arrest treated in the CVIUC at Vanderbilt University. We hypothesize that delirium, as defined by the CAM-ICU, will be present in more than 25% of patients and that use of benzodiazepines, age, shock, and time to return of spontaneous circulation will be associated with delirium.
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  • Retrospective data have been collected on all survivors of cardiac arrest who were been treated with therapeutic hypothermia at Vanderbilt since 2008. We will use this database to assess the incidence of delirium and it’s associated risk factors in post-cardiac arrest patients who survive to hospital discharge.
>
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  • Retrospective data have been collected on all survivors of cardiac arrest who were been treated with therapeutic hypothermia at Vanderbilt since 2008. We will use this database to assess the incidence of delirium and itís associated risk factors in post-cardiac arrest patients who survive to hospital discharge.
 
  • Statistical analysis will include determining the incidence of delirium in the cohort who survive to rewarming. Baseline demographics will be compared between patients who experience delirium and those who do not. Multivariate regression analysis will be undertaken to determine the association between delirium and pre-defined risk factors (age, use of benzodiazepines, shock, comorbidities, time to rosc, PEA/asystole) and any differences in baseline demographics with p-values<0.10 (assuming the incidence of delirium is high enough to provide adequate power to include these).
  • Want to estimate the incidence of delirium after the first 24 hours of therapeutic hypothermia. Also want to estimate time in coma and number of coma free (alive) days. Also looking at length of ICU stay. Want to identify risk factors for (first transition to) delirium (in logistic regression).
  • Want to address probably all of these research questions in one manuscript.
Line: 238 to 408
 
  • project on PACU length of stay and regional anesthesia.
  • based on clinic comments decided to use a stratified Cox regression model with sandwich estimator (because some patients in the dataset had more than 1 surgery).
  • Outcome of interest is time (in minutes) to successful discharge from the PACU.
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  • Predictors are regional anesthesia (yes/no), ASA Class, duration of surgery, and patient’s age based on previous studies that have looked at PACU length of stay.
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  • Predictors are regional anesthesia (yes/no), ASA Class, duration of surgery, and patientís age based on previous studies that have looked at PACU length of stay.
 
  • also matched patients on CCS Grouper (based on CPT codes) so that is how the Cox model is stratified.
Changed:
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  • I tested the predictors in the model to see if they met the proportional hazards assumption using 3 methods: looking at them graphically, looking at the Schoenfeld residuals, and including time-dependent versions of the covariates in the model. For all covariates (regional anesthesia, ASA Class, duration of surgery, and patient’s age), the proportional hazards assumption was violated.
  • Because I already am stratifying by CCS grouper, I think my best option may be to use an extended Cox model rather than stratifying on even more variables, but I’m unsure of how to interpret the hazard ratios if I use this approach.
>
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  • I tested the predictors in the model to see if they met the proportional hazards assumption using 3 methods: looking at them graphically, looking at the Schoenfeld residuals, and including time-dependent versions of the covariates in the model. For all covariates (regional anesthesia, ASA Class, duration of surgery, and patientís age), the proportional hazards assumption was violated.
  • Because I already am stratifying by CCS grouper, I think my best option may be to use an extended Cox model rather than stratifying on even more variables, but Iím unsure of how to interpret the hazard ratios if I use this approach.
 
  • We think the departure from the proportional hazards assumption may not be too serious. Whether the assumption holds would not impact the interpretation of the results but rather whether the method (Cox regression) is valid.
  • If the Schoenfeld residuals trend toward zero, you need to use an accelerated failure time model.
  • The Cox model tends to fit better for chronic conditions, where the impact of the exposure is more constant over time.
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Catherine Bulka

  • working on a project looking at hypoxemia in children during surgery & 30-day mortality.
  • includes all children who had some type of anesthetic care between 2000 and 2011, so there are some patients who had multiple surgeries.
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  • I’m planning on creating a model with a binary outcome for 30-day mortality and using oxygen saturation (a continuous variable) as a predictor along with other predictors like ASA class, age, race, sex, procedure type, pre-existing conditions, etc.
  • Because there are repeated subjects, I was thinking of using a GEE model, but I’m unsure of what kind of correlation structure to use.
>
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  • Iím planning on creating a model with a binary outcome for 30-day mortality and using oxygen saturation (a continuous variable) as a predictor along with other predictors like ASA class, age, race, sex, procedure type, pre-existing conditions, etc.
  • Because there are repeated subjects, I was thinking of using a GEE model, but Iím unsure of what kind of correlation structure to use.
 
  • is using a GEE model the best approach
  • if I do use a GEE model, how do I figure out what kind of correlation structure to use?
  • If data are large enough, should be less critical in choice but recommend Independent correlation structure, especially since some covariates will change over time (from repeated measures).
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2012 September 26

Emily Bullington, Pharmacy

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  • Project is “Preventing and managing refeeding syndrome in the acutely ill: the importance of electrolyte replenishment.”
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  • Project is ďPreventing and managing refeeding syndrome in the acutely ill: the importance of electrolyte replenishment.Ē
 

Gopi Shah, ENT research fellow

  • doing a project looking at the prevalence of hearing loss in the high schools here in Nashville.
  • would like to screen kids and then give them a survey about their perception of hearing loss.
Line: 669 to 842
 

Suseela Somarajan, General surgery

  • This is a study on finding the amplitude of gastric slow waves measured in human with different BMI before and after food.
  • For each BMI category, I have only 3 samples, have 9 total subjects.
Changed:
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  • Question: Is there any statistically significant difference in amplitude before and after food in each BMI category - performed Student’s t-test on the data (statistical significance set at p < 0.05). Since the sample size is very small the results are misleading. I need to show the data and discuss it.
>
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  • Question: Is there any statistically significant difference in amplitude before and after food in each BMI category - performed Studentís t-test on the data (statistical significance set at p < 0.05). Since the sample size is very small the results are misleading. I need to show the data and discuss it.
 
  • Gastric slow waves are measured serially. At each time point there are 16 measurements. They take averages at each time point. Before the meal, there are eight observations, and eight observations after the meal.
  • For the question about the amplitude of the waves, you should adjust for BMI. Since there are many time points within each person, the data are correlated.
  • Since the sample size is small, focus on graphical presentation. Could plot each person's trend, with time on x amplitude on y, and show BMI with color. Indicate the time of the meal with a vertical line.
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Dr. Thomas Pluim, Pediatric Critical Care

  • Case-Control study
  • Recommend propensity scores, can be used in both matching and in analysis
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Joyce Cheung-Flynn, Surgery

  • Wants to submit manuscript to Journal of Thoracic Surgery, which requires signature from statistician signing off on statistical methods. Recommended VICTR voucher for someone to look over data, rerun numbers and review manuscript; statistician could be coauthor.
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Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

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2013 August 28

Angela Joshi, resident, VA

  • observational study of patients who undergo coronary angiogram via the radial artery to determine factors that can predispose to radial artery thrombosis following angiography.
  • wants to calculate number of patients needed
  • Planning a prospective observational study
  • Have a list of factors that they want to identify the association with having thrombosis
  • Have a limited period of time over which to collect patients
  • Estimate around 1200 patients could be collected
  • Estimate about 2 percent of patients will have an event, for about 24 events
  • We advised that with this number of events, you will not have much power to learn much
  • Recommended that they rank the variables in order of their research priorities. We will calculate power based on their top priority variables.

2013 August 21

Francheska Desravines, med student at Meharry

  • study: The risk of early discharge following pediatric cardiac catheterizations in infants and young children.
  • aim is to find relative risk of a minor or major complication occurring after 6hrs post cardiac catheterization procedure.
  • .xlsx file saved as csv on ClinicsData
  • Data are retrospective chart review on kids 0-4 with cardiac cath between July 2007 and 2012
  • Want estimate of proportion of events with confidence interval
  • To identify important factors, can fit a multivariable model with factors such as age, diagnosis (one v. two ventricle), source of pulmonary blood flow
  • Data on children over 4 would be important.
  • There were 24 patients with major complications that would require major intervention
  • For a 15 kg child, the estimated model-based prob of major complication is 0.026 (95% CI 0.015-0.046). logistic regression with weight
  • Can see Vincent Agboto, who is a statistician at Meharry.

JoAnn Alvarez, for Center for Surgical Quality Outcomes Research

  • Outcome is workup quality:
      None Incomplete   Complete 
      6002       1991       1248 
  • Plan on using prop odds
  • In this case, I think we can assume exchangeable cov structure.
  • County info: 1867 unique counties in the data, with 9241 patient records. Here is the distribution of number of observations for each county:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  40  43  44  45  46  47  48  49  53  55  58  59  60  64  71 102 139 161 
671 368 212 149  73  59  49  36  30  24  18  15  15  11  15  10  12   1   9   8   5   5   4   8   1   3   4   1   3   3   5   6   3   1   1   2   1   3   1   1   1   1   1   3   2   3   1   1   1   1   1   1   1   1   1   1 
So there were 671 counties that had only one record in the data, 368 that had 2 obs, and the county that had the most representation in the data had 161 observations.
  • Could use GEE or a random intercept for county to account for practice variation by location.
  • Let m be the number of observations per cluster (subject is the cluster in many longitudinal studies), and n is the number of clusters (subjects). So m is the number of obs per county, and n is the number of counties.
  • GEE
    • Efficiency issues
      • Choice of weight mat: In this case, I think we can assume the data have an exchangeable cov structure. Loss of efficiency should be very very small (Liang and Zeger 1986) with use of independence working covar when truth is exchangeable. Or we could use exchangeable weight mat.
      • correlation of covariates within subjects: when the covars are between-subject (do not vary within a person (county here)), negligible efficiency loss. Actually, would need to check this.
      • cluster sizes: we have small m (number in each cluster), which should mean small loss of efficiency (see slide 47 of mod 4)
    • If we want to do gee with non-independence weighting, could use geeglm or geese (see slide 87 of mod 4). Can use rob cov in Frank's rms package. This is GEE with independence weight matrix / working covariance.
    • Gee requires large sample, but we have that. (n>>m Number of different counties is much bigger than the number of obs per county)
    • GEE requires MCAR.
    • Disadvantage of using gee would be that you can't use LRT since you are not specifying the full likelihood. Can use wald tests.
    • Performance with sparse clusters? Is this separate from efficiency issues? In this case, we do have sparse clusters and many clusters of size 1. The issue is variability in the cluster sizes. Jon thinks this is a validity issue.
  • GLMM (generalized linear mixed effects model)
    • GLMM requires MAR.
    • Can implement via clmm in ordinal package. logit link gives prop odds.
    • With glmm, since it's a conditional model with a non-identity link, we cannot get population-level contrasts. The contrasts are subject-specific, and I believe they approximate the pop-level values.
    • The marginal parameters are smaller in magnitude than the conditional parameters. Can think of the conditional params as "inflated" marginal params?
    • There is actually a formula for the relationship between the conditional and marginal parameters in terms of the variance of the random effect (Zeger 1988).
    • Example of interpretation of subject-specific effects "exp β represents the ratio of the expected odds of respiratory infection for an individual with vitamin A deficiency to that same individual (with same covariate values but) replete with vitamin A"
  • Can check PO assumption using chi square lrt test (difference in deviance) between the prop odds model and the cumulative logits model.

2013 July 31, August 7 and 14

no clients

2013 July 17

David Osborn, Urology

  • We estimate that this would take about $3500 for a VICTR biostat support request
  • Retrospective study looking at urinary incontinence in patients with traumatic brain injury.
  • Want to estimate the incidence of incontinence in this group
  • Outcomes are death, hospital los, 3 outcomes

Breanna Michaels and Jennifer Morse

  • *Data attached below*
  • looking at patients who received non depolarizing neuromuscular blocks during surgery and their incidence of post operative residual curarization (PORC) (means leftover block) in the PACU.
  • Main outcome is train of four, and whether it is greater than 0.9, which is the designation for PORC. Train of four is a ratio of two weakess measures.
  • Recent studies have shown that patients with PORC have a higher risk of respiratory and non-respiratory post operative complications.
  • want to know the proportion of patients with PORC, as well as the factors that affect the occurrence of PORC.
  • Factors may include gender, type/dose of non-depolarizing block, or antibiotics given during surgery.
  • Discussed whether to use the train of four ratio or the dichotomization of PORC, which is greater than 0.9,
  • Want to see if there are systematic differences between those who had outcomes measured and those who did not.
  • Could fit a regression model to identify the important factors.
  • Recommend getting a confidence interval for the proportion with PORC
  • Try plotting a histogram of the outcome variable
  • Discussed events per variable. If you use a logistic regression, dichotomizing the outcome, use the number in the smaller group to determine the number of variables you can fit in your model. A rule of thumb is to have at least ten events per variable (parameter in model).

Roop Gill

  • We estimate that the first manuscript, which will encompass two outcomes DVT and seroma will require $4000.
  • Tummy tucks
  • Have a ten-year cohort of patients
  • Want to see if skin-only abdominoplasty
  • Want to calculate the power they have for their questions with the data they have.
  • One of the main outcomes of interest is deep vein thrombosis, which is rare, so you will need a very large number of patients

2013 July 10

Bill Wester, ID/VIGH

  • Bill first consulted the group on 6/19; he would like to continue discussion his proposed study "Long-Term Renal Outcomes Among HIV-1 Infected and cART-Treated Adults in South Africa" during biostatistics clinic.
  • Need an estimate of hours/cost for VICTR biostatistics support.
  • Last time we postponed the estimate to wait for more information on the condition of the data.
  • Now we have more information on the completeness of the data. Dr. Wester is going to look through the data dictionary and the data itself to assess how clean it is.
  • Dr. Wester has decided to concentrate on Aims 1-3.
  • We estimate that this will require 100 hours of support.
  • Additional data
    • 1. How many total patients in the cohort? 2500
    • 2. How many (what %) have baseline urinary protein dipstick results? 2000
    • 3. How many (what %) have longitudinal urinary protein dipstick results/measurements; and at what frequency (I suspect they are irregularly done; but can we see how many (what %) have 1 year post ART and ? 2 year post ART values recorded/captured? 75% have a second measure
    • 4. How many (what %) have baseline creatinine results? The majority.
    • 5. How many (what %) have longitudinal creatinine results/measurements; and at what frequency (I suspect they are irregularly done; but can we see how many (what %) have 1 year post ART and ? 2 year post ART values recorded/captured? 75%
    • 6. How many were ART-naÔve? (total numbers); as it seems best to look at our main outcomes (specific aims per our protocol) among patients who initiated ART (and were ART-naÔve at the time of entering the longitudinal cohort)? Almost 95% are HAART naive at baseline.
  • Aims: Compare rates of proteinuria
  • Has dipstick measurements on about 80% of patients.
  • An alternative to a longitudinal model would be using a summary measure such as slope for each patient

Raj Keriwala, Fellow, Pulmonary and Critical Care

  • data from EDEN and FACTT trials, part of the ARDSnet.
  • ARDS Acute respiratory distress syndrome
  • Low title volume mechanical ventilation is the only therapy that has been shown to be helpful for ARDS
  • Want to look at vasopressor use over time and compare by cohort
  • retrospective study looking at the use of vasoactive agents in these cohorts and associated characteristics for any identifiable relationships.
  • will be bringing the full datasets in csv format with me tomorrow
  • What will be the best way to organize this previously collected data to facilitate analysis?
    • Discussed developing a set of inclusion criteria for the combined data to get a comparable group
    • Allow for time to implement a policy change by excluding a period of time, say, 3 months, immediately after a change
    • Recommend scripting data manipulation to ensure reproducibility
    • Will need to make sure the appropriate variables have the same variable names, the same variable types, and same variable categories/levels/formats.
    • Our department supports R. There is limited support for Stata. Few department members would be knowledgeable in SPSS.
  • Discussed whether Dr. Keriwala has access to our dept. through an existing collaboration.
  • Based on the data collected, what types of relationships can we look at in both cohorts and compare?
  • Is REDCap the right software to use for this type of project? We think it will not be worthwhile to put all the existing data in redcap.

Jennifer Morse, clinical trials specialist

  • research study on PACU delirium prevalence
  • How do I appropriately deal with the missing pain score values?
  • RASS Score is ordinal data that is broken down into 2 groups (hyper and hypo active) based on clinical significance. Is this appropriate?
  • Would a chi-square analysis be appropriate? Or an odds ratio? Or a logit analysis to compare odds ratio? Are there any other recommendations for performing the analysis?
  • Is there a relationship between pain score and RASS scores (>=1 vs <=0)?
  • Is there a relationship between 1st column (did pt wake up delirious) and gender?
  • Is there a relationship between 1st column (did pt wake up delirious ) and pain?
  • Is there a relationship between RASS scores (>=1 vs <=0)? and gender
  • Is there a relationship between CAM positive and gender?
  • Is there a relationship between CAM positive and RASS ((>=1 vs <=0) and gender?
  • Discussed the characteristics of the outcome.
  • We think it is a bad idea to combine negative scores with zero scores, since both negative and positive scores are "bad." Only two of ~400 are positive. We requested Jennifer bring us the frequencies of the different levels of this variable to aid in the decision of how to model it.
  • We recommend the investigator come to clinic so we can get more information about the clinical hypotheses behind the analyses requested
  • There is a Monday Anesthesiology studio at 4 that may be a helpful resource.
  • Part of the complexity of this analysis is that the CAM and RASS are not simply ordinal.

2013 July 3

John Koethe, ID/VIGH

  • John emailed the group on 6/20; he will briefly present some data collected in Zambia. He has pre- and post-treatment serum levels of several inflammation biomarkers from a cohort of 20 malnourished, HIV-infected adults starting antiretroviral therapy. He would like to get a quote for VICTR biostatistics support.
    • We estimated ~ 40 hours for this project.

2013 June 19

Bill Wester, ID/VIGH

  • Bill emailed the group on 6/17; he would like to discuss his proposed study "Long-Term Renal Outcomes Among HIV-1 Infected and cART-Treated Adults in South Africa" during biostatistics clinic.
  • Aim 1: Determine prevalence and incidence of proteinuria among HIV-infected adults in the pop. Proteinuria is a risk factor.
  • Aim 2: Compare rates and time to development of ESKRD (CKD4) among cases and controls in the cohort, where case/control is having proteinuria.
  • Aim 3: Compare rates of all-cause mortality between cases and controls.
  • Aim 4: Estimate the impact of of cART on preventing adverse renal outcomes.
  • Proteinuria isdefined as having a urinary dipstick of 1+ or greater. The possible values are 0, 1+, 2+, and 3+.
  • Recommend avoiding framing study as "case/control" and instead preserving the information in the dipstick result.
  • Could use creatinine values to impute missing dipstick results. There may be few instances of missing dipstick but nonmissing creatinine.
  • One of the main objectives is looking at the time-dependent relationship between proteinuria and eGFR level/development of an eGFR event.
  • Ideally we would want to use time to event, but the outcome is not regularly measured. This would be an argument for treating this as a longitudinal study.
  • We will estimate the hours required after we have more information about the condition of the data.
  • Existing cohort size ~ 5000.

2013 June 12

Catherine Bulka, Shane

  • sample size calculation for a study on the prevalence of red-green color vision deficiency
  • The proportion of color-blindness in men in the US is about 7%
  • The study is ongoing and is based on a convenience sample of over 300 patients, many of whom are females.
  • Want to estimate the proportion of male physicians at Vanderbilt with color blindness and the proportion among other male providers at Vanderbilt.
  • Can estimate the difference in proportions between the physicians and other providers. Can see if the CI for proportions in each group includes the value for the general population (7%).
  • Can power based on width of confidence interval.
  • Recommend a better sampling scheme. Could use a simple random sample.
  • Want to ensure high response rate.
  • PS software does power and sample size calculations and is provided for free here: http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize.
  • To compare the two proportions, you need many more subjects. Can increase power by making the ratio of physicians to other providers closer to 1:1.
  • Just to estimate the proportion within +-3% if the true value is 7%, you would need about 278. For +-5%, you would need 101.
  • Online calculator is here: http://epitools.ausvet.com.au/content.php?page=1Proportion

2013 June 5

No client investigators.

2013 May 22

Kirk Kleinfield

  • Wants to identify predictors of epilepsy for an abstract
  • Recommend he focus on variables that he has nonmissing data for and that are a priori identified by experts.

Kurt Niesner, VA GRECC, neurosurgery

  • retrospective study of 58 patients who received a fiesta MRI
  • Exposure is compression: no compression, possible compression, and definite compression, and also whether or not the patient's symptoms matched the presentation
  • Wants to evaluatie the efficacy of pre-operative MRI in predicting post-operative outcome in patients with Trigeminal Neuralgia undergoing Microvascular Decompression surgery.
  • This study may be able to evaluate the association between compression, symptomaticness, and outcome.
  • comparing the presence and locus of trigeminal nerve compression during pre-op MRI to early (1st follow-up visit) and late outcome (1 to 5 years post-surgery).
  • Early outcome is improvement in pain within one month, measured on a scale of 1-3 inclusive. Late outcome is Brief Pain inventory, which is on a scale of 0-150 inclusive.
  • Patients are almost all in severe pain before the surgery.
  • We recommend considering whether there are important factors that would likely influence your outcomes.
  • Could run a proportional odds model with the short term pain improvement as the outcome with the compression and symptomaticness as covariates, as well as their interaction.
  • The mentor could apply for a VICTR voucher for biostatistics support
  • discussed the need for a letter of support for amount above $2000.
  • We estimate that this project would require about $4000.
  • The long term outcome is problematic because of the proportion of missingness.
  • To estimate an adequate sample size for this type of study, you need to consider the smallest effect that you consider important.
 

2013 May 15

Roshi Markley

  • Needs a quote for VICTR biostat support
  • had a design studio with Frank Harrell and Ayumi Shintami who have helped with the design of the study.
Changed:
<
<
  • Looking at hospital free days following assessment of severe aortic stenosis.
  • Want to identify what factors are associated with lower hospital free days and develop a multivariable model to predict individuals that are at risk of poor outcome.
>
>
  • Looking at hospital free days following assessment of severe aortic stenosis. Not sure about distribution - similar outcome, delirium/coma-free days, is quite bimodal, but not sure if hospital-free days over one year will be similar.
  • Want to identify what factors are associated with lower hospital free days and develop a multivariable model to predict individuals that are at risk of poor outcome, including therapy of TAVR, surgery (gold standard) or no therapy.
  • About 30% mortality rate expected during first year (50% among patients with no therapy)
  • Patients mainly in 80s and 90s
  • Secondary outcomes: one-year mortality, major vascular complications (eg bleeding)
  • Planning to submit manuscript
  • Estimate 70 hours for work, manuscript prep and anticipated revisions
 

2013 April 17

Jeremy S. Pollock, Internal Medicine, Wes Ely, Mentor

  • Has VICTR biostats request
  • aim of this retrospective study is to determine the incidence and risk factors of delirium in survivors of cardiac arrest treated in the CVIUC at Vanderbilt University. We hypothesize that delirium, as defined by the CAM-ICU, will be present in more than 25% of patients and that use of benzodiazepines, age, shock, and time to return of spontaneous circulation will be associated with delirium.
Changed:
<
<
  • Retrospective data have been collected on all survivors of cardiac arrest who were been treated with therapeutic hypothermia at Vanderbilt since 2008. We will use this database to assess the incidence of delirium and itís associated risk factors in post-cardiac arrest patients who survive to hospital discharge.
  • Statistical analysis will include determining the incidence of delirium in the cohort who survive to rewarming. Baseline demographics will be compared between patients who experience delirium and those who do not. Multivariate regression analysis will be undertaken to determine the association between delirium and pre-defined risk factors (age, use of benzodiazepines, shock, comorbidities, time to rosc, PEA/asystole) and any differences in baseline demographics with p-values<0.10 (assuming the incidence of delirium is high enough to provide adequate power to include these).
>
>
  • Retrospective data have been collected on all survivors of cardiac arrest who were been treated with therapeutic hypothermia at Vanderbilt since 2008. We will use this database to assess the incidence of delirium and it’s associated risk factors in post-cardiac arrest patients who survive to hospital discharge.
  • Statistical analysis will include determining the incidence of delirium in the cohort who survive to rewarming. Baseline demographics will be compared between patients who experience delirium and those who do not. Multivariate regression analysis will be undertaken to determine the association between delirium and pre-defined risk factors (age, use of benzodiazepines, shock, comorbidities, time to rosc, PEA/asystole) and any differences in baseline demographics with p-values<0.10 (assuming the incidence of delirium is high enough to provide adequate power to include these).
 
  • Want to estimate the incidence of delirium after the first 24 hours of therapeutic hypothermia. Also want to estimate time in coma and number of coma free (alive) days. Also looking at length of ICU stay. Want to identify risk factors for (first transition to) delirium (in logistic regression).
  • Want to address probably all of these research questions in one manuscript.
  • Could address delirium using time to delirium or using a Markov transition model.
Line: 39 to 238
 
  • project on PACU length of stay and regional anesthesia.
  • based on clinic comments decided to use a stratified Cox regression model with sandwich estimator (because some patients in the dataset had more than 1 surgery).
  • Outcome of interest is time (in minutes) to successful discharge from the PACU.
Changed:
<
<
  • Predictors are regional anesthesia (yes/no), ASA Class, duration of surgery, and patientís age based on previous studies that have looked at PACU length of stay.
>
>
  • Predictors are regional anesthesia (yes/no), ASA Class, duration of surgery, and patient’s age based on previous studies that have looked at PACU length of stay.
 
  • also matched patients on CCS Grouper (based on CPT codes) so that is how the Cox model is stratified.
Changed:
<
<
  • I tested the predictors in the model to see if they met the proportional hazards assumption using 3 methods: looking at them graphically, looking at the Schoenfeld residuals, and including time-dependent versions of the covariates in the model. For all covariates (regional anesthesia, ASA Class, duration of surgery, and patientís age), the proportional hazards assumption was violated.
  • Because I already am stratifying by CCS grouper, I think my best option may be to use an extended Cox model rather than stratifying on even more variables, but Iím unsure of how to interpret the hazard ratios if I use this approach.
>
>
  • I tested the predictors in the model to see if they met the proportional hazards assumption using 3 methods: looking at them graphically, looking at the Schoenfeld residuals, and including time-dependent versions of the covariates in the model. For all covariates (regional anesthesia, ASA Class, duration of surgery, and patient’s age), the proportional hazards assumption was violated.
  • Because I already am stratifying by CCS grouper, I think my best option may be to use an extended Cox model rather than stratifying on even more variables, but I’m unsure of how to interpret the hazard ratios if I use this approach.
 
  • We think the departure from the proportional hazards assumption may not be too serious. Whether the assumption holds would not impact the interpretation of the results but rather whether the method (Cox regression) is valid.
  • If the Schoenfeld residuals trend toward zero, you need to use an accelerated failure time model.
  • The Cox model tends to fit better for chronic conditions, where the impact of the exposure is more constant over time.
Line: 50 to 249
 

Catherine Bulka

  • working on a project looking at hypoxemia in children during surgery & 30-day mortality.
  • includes all children who had some type of anesthetic care between 2000 and 2011, so there are some patients who had multiple surgeries.
Changed:
<
<
  • Iím planning on creating a model with a binary outcome for 30-day mortality and using oxygen saturation (a continuous variable) as a predictor along with other predictors like ASA class, age, race, sex, procedure type, pre-existing conditions, etc.
  • Because there are repeated subjects, I was thinking of using a GEE model, but Iím unsure of what kind of correlation structure to use.
>
>
  • I’m planning on creating a model with a binary outcome for 30-day mortality and using oxygen saturation (a continuous variable) as a predictor along with other predictors like ASA class, age, race, sex, procedure type, pre-existing conditions, etc.
  • Because there are repeated subjects, I was thinking of using a GEE model, but I’m unsure of what kind of correlation structure to use.
 
  • is using a GEE model the best approach
  • if I do use a GEE model, how do I figure out what kind of correlation structure to use?
  • If data are large enough, should be less critical in choice but recommend Independent correlation structure, especially since some covariates will change over time (from repeated measures).
Line: 63 to 262
 
  • Abstract deadline April 3. Applying for VICTR voucher to get the analysis done in time to submit the abstract.
  • Question 1: Is there a re-op or not. Question 2: Does age at initial operation correlate with type of reoperation.required
  • Might be interested in the age at which the re-op takes place
Changed:
<
<
  • For abstract, look at descriptive statistics * VICTR estimate 60 hours
>
>
  • For abstract, look at descriptive statistics * VICTR estimate 60 hours
 

2013 March 13

Jayant Bagai

Line: 1487 to 1684
 
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Added:
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META FILEATTACHMENT attachment="PORC_Dataset_16Jul2013.xlsx" attr="" comment="from Jennifer Morse and Breanna Michaels" date="1374077892" name="PORC_Dataset_16Jul2013.xlsx" path="PORC_Dataset_16Jul2013.xlsx" size="163162" stream="IO::File=GLOB(0x95f9444)" tmpFilename="/tmp/bw4PRYUvHe" user="JoAnnAlvarez" version="1"
Revision 151
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META TOPICPARENT name="ClinicSurg"

Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

Added:
>
>

2013 May 15

Roshi Markley

  • Needs a quote for VICTR biostat support
  • had a design studio with Frank Harrell and Ayumi Shintami who have helped with the design of the study.
  • Looking at hospital free days following assessment of severe aortic stenosis.
  • Want to identify what factors are associated with lower hospital free days and develop a multivariable model to predict individuals that are at risk of poor outcome.

2013 April 17

Jeremy S. Pollock, Internal Medicine, Wes Ely, Mentor

  • Has VICTR biostats request
  • aim of this retrospective study is to determine the incidence and risk factors of delirium in survivors of cardiac arrest treated in the CVIUC at Vanderbilt University. We hypothesize that delirium, as defined by the CAM-ICU, will be present in more than 25% of patients and that use of benzodiazepines, age, shock, and time to return of spontaneous circulation will be associated with delirium.
  • Retrospective data have been collected on all survivors of cardiac arrest who were been treated with therapeutic hypothermia at Vanderbilt since 2008. We will use this database to assess the incidence of delirium and itís associated risk factors in post-cardiac arrest patients who survive to hospital discharge.
  • Statistical analysis will include determining the incidence of delirium in the cohort who survive to rewarming. Baseline demographics will be compared between patients who experience delirium and those who do not. Multivariate regression analysis will be undertaken to determine the association between delirium and pre-defined risk factors (age, use of benzodiazepines, shock, comorbidities, time to rosc, PEA/asystole) and any differences in baseline demographics with p-values<0.10 (assuming the incidence of delirium is high enough to provide adequate power to include these).
  • Want to estimate the incidence of delirium after the first 24 hours of therapeutic hypothermia. Also want to estimate time in coma and number of coma free (alive) days. Also looking at length of ICU stay. Want to identify risk factors for (first transition to) delirium (in logistic regression).
  • Want to address probably all of these research questions in one manuscript.
  • Could address delirium using time to delirium or using a Markov transition model.
  • Would like to request Jennifer Thompson because of her expertise. This would need to be approved by Frank.
  • We estimate this would require about 50 hours of biostatistics support. We discussed the cost sharing information.

2013 April 3

Harry Wright, Otolaryngology

  • Estimate 35 hours of analysis for purposes of VICTR biostatistics support application.
  • See DataTransmissionProcedures
  • Conducting a retrospective chart review for 71 patients who had surgery to remove a skull-based lesion. The surgery places the facial nerve at risk. All these patients had to have a repair procedure.
  • The main question involves the impact of the time between the surgery and the repair on recovery of function. The functionality is measured on a 1-6 scale. Of interest is whether the patient can voluntarily close his eye (3 of 6). All of these patients have values of 3, 4, 5, or 6.
  • Want to consider patient's age, surgeon,
  • The time between the surgeries is thought to be random, which is good for making inference on the impact of time on the outcome.
  • Proportional odds would be a good choice.
  • You don't really have power to adjust for surgeon id with fixed effects.
  • Would want to look at the distribution of the outcome: frequencies of each 3, 4, 5, and 6.
  • If you do think that surgeon id is an important factor on the outcome, you could model this appropriately using random effects (a random intercept for surgeon id). The purpose in your research objective is not to estimate an effect for a particular surgeon, but to appropriately account for it in the model so that your inference about the variable of interest, time, is correct. However, estimating the variance of the random effects may be hard with only five surgeons.
  • Another way to account for this extra variation due to surgeon is to adjust for the surgeon's case mix.
  • May want to adjust for the severity of each case.

Catherine Bulka

  • project on PACU length of stay and regional anesthesia.
  • based on clinic comments decided to use a stratified Cox regression model with sandwich estimator (because some patients in the dataset had more than 1 surgery).
  • Outcome of interest is time (in minutes) to successful discharge from the PACU.
  • Predictors are regional anesthesia (yes/no), ASA Class, duration of surgery, and patientís age based on previous studies that have looked at PACU length of stay.
  • also matched patients on CCS Grouper (based on CPT codes) so that is how the Cox model is stratified.
  • I tested the predictors in the model to see if they met the proportional hazards assumption using 3 methods: looking at them graphically, looking at the Schoenfeld residuals, and including time-dependent versions of the covariates in the model. For all covariates (regional anesthesia, ASA Class, duration of surgery, and patientís age), the proportional hazards assumption was violated.
  • Because I already am stratifying by CCS grouper, I think my best option may be to use an extended Cox model rather than stratifying on even more variables, but Iím unsure of how to interpret the hazard ratios if I use this approach.
  • We think the departure from the proportional hazards assumption may not be too serious. Whether the assumption holds would not impact the interpretation of the results but rather whether the method (Cox regression) is valid.
  • If the Schoenfeld residuals trend toward zero, you need to use an accelerated failure time model.
  • The Cox model tends to fit better for chronic conditions, where the impact of the exposure is more constant over time.

2013 March 20

Catherine Bulka

  • working on a project looking at hypoxemia in children during surgery & 30-day mortality.
  • includes all children who had some type of anesthetic care between 2000 and 2011, so there are some patients who had multiple surgeries.
  • Iím planning on creating a model with a binary outcome for 30-day mortality and using oxygen saturation (a continuous variable) as a predictor along with other predictors like ASA class, age, race, sex, procedure type, pre-existing conditions, etc.
  • Because there are repeated subjects, I was thinking of using a GEE model, but Iím unsure of what kind of correlation structure to use.
  • is using a GEE model the best approach
  • if I do use a GEE model, how do I figure out what kind of correlation structure to use?
  • If data are large enough, should be less critical in choice but recommend Independent correlation structure, especially since some covariates will change over time (from repeated measures).

Roop Gill, Marcia Spear, Plastic Surgery

  • Cranial facial data base (10 yrs). Data is stored in REDCap
  • Age at which head was fixed, type of suture, race.
  • Outcome: re-operations: redone, voids, voids/reshaping, minor and whether there is an optimal age at which to do the initial surgery
  • Need for initial surgery diagnosed anywhere from birth to 6 months of age.
  • Abstract deadline April 3. Applying for VICTR voucher to get the analysis done in time to submit the abstract.
  • Question 1: Is there a re-op or not. Question 2: Does age at initial operation correlate with type of reoperation.required
  • Might be interested in the age at which the re-op takes place
  • For abstract, look at descriptive statistics * VICTR estimate 60 hours

2013 March 13

Jayant Bagai

  • Needs estimate for VICTR
  • Health outcomes research study of veterans at the Nashville VA hospital.
  • The objective is to compare death, MI, stroke, repeat revascularization and major bleeding in 4 cohorts of patients startified by a combination of arterial access site (radial vs. femoral artery) and type of anticoagulant used for coronary intervention (heparin vs. bivalirudin). One of the key comparisons the incremental benefit of a radial-angiomax strategy in reducing bleeding compared with a femoral-angiomax strategy .
  • The 4 cohorts are:
  1. femoral + heparin n=293
  2. femoral + angiomax n= 255
  3. radial + heparin n = 469
  4. radial + angiomax n= 489
  • We know from previously published data that-
    • Radial access reduces bleeding compared with femoral access and
    • Femoral angiomax reduces bleeding compared with femoral heparin use.
  • What is not known is if the combination of radial and angiomax is superior to femoral angiomax and if so by what magnitude and if that translates into other benefits such as reduction in mortality.
  • The following analysis need to be performed-
    • Comparison of baseline variables in 4 cohorts
    • Comparison of outcomes between cohorts in composite and individual outcomes: length of stay, in-hospital death, MI
    • Multivariable logistic-regression models to identify predictors of major bleeding.
    • Cox regression models to determine association between bleeding and death, MACE (major adverse cardiovascular events- composite of death, MI, stroke and unplanned/urgent revascularization), length of stay and readmissions. * Need to consider other systematic differences between the four cohorts.
  • May want to control for number of stents placed
  • Data includes repeats of individuals
  • May be underpowered to detect differences in overall survival.
  • We estimate that this analysis would require about 60 hours of statistics work.
  • Dr. Bagai also has a study involving a device used to apply pressure for venus closure. They want to compare three devices and manual pressure on bleeding, patient satisfaction, cost. They need to know the sample size required to provide adequate power. If the main outcome is bleeding (y/n), then the sample size will depend on the number of bleeds

Alison Kemph, Hearing and Speech

  • looking at contamination in ear molds in children. What is the percent of ear molds with bacteria? Need to know the sample size required to estimate the proportion. If the true proportion is about 0.5, would need about 43 patients to estimate the proportion to precision of +-0.15. If the true proportion is 0.7, you would only need about 36 patients to get the same precision.
  • May want to record the date of the culture and the child's age.
  • We estimate that this project would take about 40 hours of work.
  • If the scope of the manuscript/project is limited to only estimating the proportion with confidence interval, it can be done in 20 hours.
  • See DataTransmissionProcedures

Dupree Hatch, Neonatology

  • Studying adverse events in intubations
  • Describe the rate of adverse events
  • Have new standardization procedure to implement
  • Need power to detect difference between proportions. For 80% power to detect a reduction of 50% or greater if the baseline rate is 30% with 95% confidence, you would need 134 patients per time period.

2013 March 6

Jennifer Morse, Clinical Trials Specialist, Periop Clinical Research Inst.

  • Survey questions sent to Vanderbilt and MTSA and want to compare the results. (Vanderbilt=74, MTSA=58) 88 and 63% response rates.
  • Want to see if there is a difference in how the two groups answered the questions.
  • Survey of nurse anesthesists about opinions on classes.
  • Students were also asked to identify the five most important aspects out of 22 and least important aspects.
  • Plan on making a basic bar graph for each question but am interested if there are further suggestions for organizing the results, or statistical tests that should be performed.
  • Interested in analysis techniques for displaying results for the ranking question: Participants were asked to pick their top 5 and bottom 5. How can this data be presented collectively? Most common answer? Weighted results?
  • A good way to display the individual questions would be a dot plot. You could, for the graph, dicotomize all 22 questions into either highly or critically important and give one "line" with two dots giving this percent, one each for CRNAs and SRNAs. Or you could give the means
  • Can sort the items in the chart by either the CRNA's score (proportion or mean), or the SRNA's score, or sort by the difference between the two scores.
  • Could do Wilcoxon Rank-Sum Test for each individual test to check for a difference between groups (for one time point).

  • Consider: What information do you have on characteristics of survey responders vs. non-responders? What evidence do you have that the response rates were the same for Vandy and MTSA?

2013 February 27

Eileen Duggan, General Surgery

  • Laproscopic vs. open pyloromyotomies in terms of cost, LOS, complications, etc
  • So far have done univariate analyses (t-tests or Wilcoxon, Fisher's)
  • Strongly recommend multivariate analyses (linear or logistic regression) rather than univariate; may need to transform variables (LOS) to fit model assumptions
  • Create list of main hypotheses (research questions) and confounders before doing model fitting, working with mentors and looking at literature to get a plan together
  • 283 patients total, 127 vs. 156 (ten different surgeons; surgery type is nearly always determined by surgeon more than other factors)
  • Might use surgeon as a random effect in mixed effects model - no estimates, but adjust for surgeon

Catherine Bulka, Anesthesiology

  • help analyzing length of stay in the PACU (outcome) and regional vs. any other type (general) anesthesia (predictor) on a large dataset. We have identified potential confounders such as surgical procedure, ASA Class, age, etc. We wanted to try to match patients on CPT code to account for the different surgical procedures. The only way I know how to do a matched analysis is using conditional logistic regression, but the outcome for this analysis is continuous. Is there a way to do a matched analysis using linear regression?
  • LOS has a strange distribution that is best handled by the Cox proportional hazards model. And if you have more than, say, 1% of the patients die (which right-censors their LOS) you can censor them on the day of death. That way you don't give credit for a short LOS for those who died. The outcome then becomes "time until successful discharge".
  • With the Cox model you can stratify on CPT code group with no matching necessary. It would be nice to have at least 200 subjects per stratum if possible, so probably need fairly broad groups of CPT codes.
  • There are 198,712 surgical cases and 3,300 unique CPT codes. Will group the CPT codes into broader categories.
  • Looking at all surgeries at VUMC since 2008. Hypothesis is that patients receiving regional anesthesia will have shorter PACU stay than those getting general.
  • Patients can be included for multiple surgeries, so need to account for within-patient correlation.
  • First step is to check death rate - if more than 1-2%, need to do Cox regression, but harder to control for repeated measures. Poisson regression is another possibility (count number of minutes), possibly including indicator for death in the model but would need to interact that term with everything else.
  • Lots of potential confounders to consider (BMI, surgery type and length, ASA class).
  • Regional vs. general anesthesia is very often decided by type of procedure - not sure you'll be able to tell which is the real cause of any difference. Maybe focus on surgeries where patients have a good chance of getting either type.
  • Only ~20,000 out of 198,000 surgeries had regional anesthesia.
  • Check with anesthesiology collaborators - JonathanSchildcrout, DavidAfshartous, MattShotwell

2013 February 20

Yuri van der Heijden, Division of Infectious Diseases. Mentor: Tim Sterling

  • K08 submitted, score 'in range' for resubmission in May
  • Looking for support from BCC.
  • Drug resistance in TB patients (specifically, fluoroquinolone) in cohort in South Africa. Aim 1: describing yearly incidence of fluoroquinolone resistance 2007-2011. Aim 2: Describe risk factors for FQ resistance, primary of those is HIV status. Aim 3: Death/culture conversion as outcomes.
  • Bryan Shepherd has been involved in pre-grant preparation. Based on that, It does not appear that he needs VICTR funds for pre-grant submission work. We recommend that he first talk with Bryan as to how much would be required from VICTR to perform the analysis. [BRYAN: I have discussed the analyses with Yuri. As part of his K-award he proposes learning how to do these analyses and implementing them with my supervision. Therefore, I will have protected time (%-effort) on his K-award, and he will only need 100 hours of support from the Biostatistics Core, which will be used in case Yuri needs some additional help with specific parts of his proposal. This is, of course, a rough estimate, but is the amount budgeted into his application.]

2013 February 13

Michael Dewan, Neurosurgery. PI: J Mocco

  • developing a RCT comparing seizure frequency and clinical outcome in patients with subarachnoid hemorrhage who are given levetiracetam (treatment) or no drug (control). Plan to observe for 30 days after discharge.
  • Wants to discuss power calculations, randomization, and a couple other topics
  • Currently, prophylactic meds given if patient has history of seizure; otherwise, just physician preference
  • Between 8-20% patients experience seizures after subarachnoid hemorrhage, typically in the first few days. Up to 1/3 of these are before they get to the hospital.
  • Plan to randomize patients with SAH who have not yet had a seizure (documented absence/no history of seizure). Patients with previous history or who seized in the field will be excluded.
  • Interested in incidence of seizure and modified Rankin scale (6-point scale of ability to perform daily functions) at discharge and 30 days.
  • At VUMC, see approximately 200 patients/year with SAH; estimate about 10% of patients have seizures.
  • Ideally want to balance groups in terms of Hunt-Hess score (1-5); suggested minimization randomization using a web program (JoAnn has experience with this using VUMC investigational pharmacy). Stratified randomization also an option, as is basic randomization.
  • Do not currently plan to blind patients or nurses, but don't expect any differences in monitoring. Recommend some sort of placebo and blinding if possible. VICTR may be a good resource for this, maybe using IV formulation rather than pill.
  • Recommend applying for 20-hour VICTR voucher for initial study planning, sample size, analysis plan.

Joyce Cheung-Flynn, Surgery

  • Wants to submit manuscript to Journal of Thoracic Surgery, which requires signature from statistician signing off on statistical methods. Recommended VICTR voucher for someone to look over data, rerun numbers and review manuscript; statistician could be coauthor.

Mick Edmonds, PMI

  • Has data and would like to answer several sensitive questions. Suggested speaking with CQS statisticians, who have expertise in specific types of data/questions. JoAnn can send email to set him up with a contact (maybe WilliamWu ?) and get accurate quote for number of hours involved. In the meantime, plans to apply for 60-hour VICTR voucher to get started.

2013 February 6

Catherine Bulka, Anesthesiology

* NSQIP data -- looking for association between post-anesthesia drug and post-op pneumonia * ~ 1500 patients with 1600 observations. 65 got pneumonia; * Interested in estimating relative risk of pneumonia; how to handle modeling with repeated records for patients * 43 had 2 surgeries * Potentially use frailty model in a survival analysis rather than Poisson. * Some concern regarding who is given the post-op drug and who is not given it.

Lawrence House, Anesthesiology

* Ability of cardiac troponin's to ID those with post-op MIs. * Objective 1: ID variables which are associated with a post-op cardiac troponin ordered * Objective 2: How do cardiac troponin values correlate with MIs, etc. * Does not include cardiac surgeries but does include vascular surgeries. Suggest removing vascular surgeries. * Impact is from a resource uttilization point of view. * There are duplicates in the data. Recommend using the sandwich estimator for SEs in modeling.

William Sullivan,, 4th year medical students

* Database of medical student notes. * Compare last year's and this year's 2nd year medical student notes * Interrater reliability between four coders who will be coding the 'sophistication' of the notes. * Coding anakyzes language using rating from 0 - 2 that assesses arguments and language of arguments on semantic complexity * Will not be able to assess whether the diagnosis is correct * Coders will be trained. * Need sample size requirements; may be driven by time/resource limitations. Need to de-identify the notes so need some estimate on a sample size. * There will be multiple notes from the same student but all from different patients. * Suggest pulling a small sample and looking at inter-rater reliability for estimates on how long it will take and how different the raters are. Rather than use kappa could use intraclass correlation (ICC). * Ultimately looking for publication but also impacting training policy in the SOM.

2013 January 30

Andre Marshall, General Surgery

  • Planning on a logistic regression model
  • Outcome is 30 day readmission after apendectomy for acute apendicitis
  • 1800 pediatric patients, 103 of which had readmissions within 30 days
  • Wants to identify risk factors for readmission. Specifically insurance status.
  • Perforation status is already known to be a factor.
  • Also wants to describe the group of patients who are readmitted
  • Has thirty days follow up for all patients, and is also interested in time to readmission.
  • Insurance is a substitute for socio economic status. categories are are public insurance and private insurance
  • could have interaction between perforation, length of stay, and insurance status.
  • Planning on adjusting for ethnicity. This may wash out the effect of the insurance because they are often highly correlated.
  • We don't recommend computing power after you already have your data. The width of your confidence intervals will give you an idea of the precision your data are giving.

David Young, Resident, Psychiatry Department

  • mentor is Peter Martin
  • Study on detox med in psych hospital
  • Testing hypothesis that response to detox protocol is associated with dx
  • Response is scored as drowsy, irritable, etc. Patients will get several observations over time, and the mode will be taken as the response.
  • possible diagnoses are depression, PTSD, dementia, about 5.
  • Hope to use the patient's response to detox protocol to determine the diagnosis.
  • Can make a frequency table of the combinations of diagnoses and responses.
  • Currently have 100 patients meting inclusion criteria with existing data
  • Can assess correlation without adjusting for other factors using a chi square test.
  • Each person would only be counted once in the table and analysis
  • You can group different outcomes to avoid cells (combinations) with very few (less than 5) observations, but agree upon the grouping with your colleagues on one set of grouping before running the tests

2013 January 23

Steven Goudy, Otolaryngology

  • Working on basic science R01 due Feb. 5 - mice with knockout genes, looking to determine temporal relationship between gene expression and bone/blood vessel development in upper jaw
  • Frank recommended two-way ANOVA design with time and treatment (and time*treatment interaction), measuring bone density, protein expression, etc as outcomes
  • One potential complication is multiple comparisons - want to study up to 25 genes, and calculating sample size for this is complex
  • Emphasize in grant that each mouse is only studied under one condition - no need to account for repeated measurements
  • For grant purposes, can use boilerplate language to specify BCC for stats help
  • Also look at VANGARD clinic in cancer center for genomics assistance

Damon Michaels & Ricky Sierra, Anesthesiology

  • Looking at pain control using nerve blocks after knee replacement; sciatic nerve splits in two above knee, and if it is blocked before this split, can't assess nerve injury
  • New treatment: tibial nerve block; everyone gets femoral nerve block
  • Outcomes: pain control (patient rating) plus use of opioids, measured in PACU and then every six hours for first 24h
  • Previous studies have showed difference in pain scores and ~10mg morphine equivalents over 24h (SD ~15mg)
  • Main outcome for power calculation: total amount of opioids given in first 24 hours (measured in morphine equivalents); however, using this might result in reduced power for ordinal outcome of pain rating scale
  • Preliminary power analyses in clinic - probably 50 patients per group for 90% power, 35 patients will get about 76% power
  • Email Matt Shotwell (matt.shotwell@vanderbilt.edu) protocol, set up appointment for further discussion
 

2013 January 16

Scott Zuckerman, neurosurgery

  • project polling expert neurosurgeons on how they would treat recurrent aneurysms at 1 year with Dr. J Mocco.
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4.Burn ICU (June 2010)
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Catherine Bulka, Anesthesiology

  • Has a rejected manuscript that needs to be improved
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  • One option could be to collaborate with Jackson heart study (~Framingham, recreated in Jackson, MS); might be better estimate of true prevalence, but this procedure isn't performed in Jackson
  • Main questions: What is the prevalence of aortic stenosis among African-Americans? Is there a racial disparity in how TAVR technology is applied, when adjusting for severity of disease, SES, etc?
  • These two questions require different data, different studies. #1 requires community-based survey.
Changed:
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  • National Cardiovascular Data Registry (NCDR) is in development, but not mature enough to use yet; Medicare Current Beneficiary Survey may be a good bet (age is available), but need enrollment file, ICD9s, procedures - need to get as close to entire population as possible, not just sick patients. Dave Penson at VUMC might be able to help with this.
>
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  • National Cardiovascular Data Registry (NCDR) is in development, but not mature enough to use yet; Medicare Current Beneficiary Survey may be a good bet (age is available), but need enrollment file, ICD9s, procedures - need to get as close to entire population as possible, not just sick patients. Dave Penson at VUMC might be able to help with this.
 
  • Any major database like Medicare will take lots of time/power to work with
  • Applying for VICTR studio - no estimate of statistician time needed for that

2012 December 12

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Dr. Thomas Pluim, Pediatric Critical Care

  • Case-Control study
  • Recommend propensity scores, can be used in both matching and in analysis
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Joyce Cheung-Flynn, Surgery

  • Wants to submit manuscript to Journal of Thoracic Surgery, which requires signature from statistician signing off on statistical methods. Recommended VICTR voucher for someone to look over data, rerun numbers and review manuscript; statistician could be coauthor.

 

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Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

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2013 January 16

Scott Zuckerman, neurosurgery

  • project polling expert neurosurgeons on how they would treat recurrent aneurysms at 1 year with Dr. J Mocco.
  • N=40 physicians to be surveyed about when and how they would re-treat a brain aneurysm that was treated one year (more or less) prior, and that had certain characteristics (total of 400 permutations of 6 fundamental aneurysm characteristics); how best to construct questionnaire (with all 400 questions, some sort of latin-rectangle-like design, etc.).
  • would like quote

2013 January 2

Yaa Kumah-Crystal

  • Has questions about stata.
  • has longitudinal data on measurements taken before and after an intervention.
  • the times are different for each patient
  • the origin is the time of intervention.
  • your data need to be formatted with one observation on each row, with a variable for id, time/date, and the response variable.
  • Main first question is how to reshape wide to long in stata.
  • The problem may lie in the variable names. For stata's reshape function, it depends on the number of the time being on the end of the variable name. Make your variable name in that form first.
  • Then use the reshape long function.

Joshua Squiers

Over a 6-year period, Vanderbilt Medical Center developed 
multidisciplinary ICU teams to provide expanded coverage to five of 
their adult tertiary care ICUs, including the Surgical Intensive Care 
Unit, the Neurosurgical Intensive Care Unit, the Medical Intensive 
Care Unit, the Cardiovascular Intensive Care Unit, and the Burn Unit.
Currently, within this model one MD intensivist partners with a number 
of ACNPs to provide care for a significantly larger number of ICU 
patients than the MD could usually provide alone. This medical team's 
core consists of ACNP intensivists and MDs intensivists who provide 
billable medical services for ICU patients, in conjunction with other 
ancillary services.

This project is a descriptive observational study assessing the 
association of NP care on critical care patient outcomes. This study 
will utilize the Vanderbilt ICU database to provide data on a variety 
of outcome measures, including mortality and inpatient healthcare 
associated quality indicators. The ICU database contains records from 
more 150,000 ICU patient visits since 2005, and contains descriptive, 
morbidity, mortality, and quality data. In conjunction with the ICU 
database, the Social Security Administration's Death Master File will 
be searched to determine 30 day and 1 year mortality.

This study will utilize a pre-test/post-test design to compare outcome 
and quality indicators in each of the above ICUs. Each ICU initiated 
their NP teams at different times during the past six years. The 
initiation time point will serve as the demarcation for 
pre/post-testing in each of the ICUs. There will be a six month 
washout period following the team initiation to control for the 
Hawthorne effect, and allow time for team establishment. Pre-test and 
post-test analysis will contain all of the patients for that given ICU
1 year prior to the initiation of the NP/MD teams, and following the 
washout period, will include 1 year of post-test data. All data will 
be de-identified prior to final analysis.

Data from the following ICUs will be collected, utilizing the 
following team initiation dates.

1.Cardiovascular ICU (January 2007)

2.Surgical ICU (January 2010)

3.Medical ICU (October 2010)

4.Burn ICU (June 2010)

5.Neuro ICU (August 2009)

Catherine Bulka, Anesthesiology

  • Has a rejected manuscript that needs to be improved
  • Has data from a prospective group that was matched to a retrospective group
  • Research question is whether continuous monitoring of hemoglobin during surgery reduces the number and amount of transfusions.
  • Belief is that without monitoring, surgeons tend to overprescribe transfusions
  • type of surgery is elective orthopedic.
  • since the prospective, controlled trial was not blinded, they later decided to also include retrospective data.
  • One strategy to address this concern could be to compare the rate of transfusion between the retrospective group and the control group. Give the odds ratio for receiving a transfusion with a confidence interval.
  • We recommend the main analysis be only on the prospective patients.
  • For your main analysis, instead of providing a p value from a Fisher's exact test, give an odds ratio with confidence interval.
  • Reviewers are also concerned about heterogeneity caused by different types of surgeries. One way to address this is point to the fact that the groups were randomized and also show the breakdown of types of surgeries by group. (The distribution of types of surgeries don't appear to differ across groups.)
  • The reviewers are asking for p values for the baseline comparisons. This would require a statistical argument that the two group populations are by definition the same, so testing the hypothesis would not make sense, plus some reference. If you do end up doing statistical tests, use wilcoxon rank sum, kruskal wallis, or chi square tests.
  • The original randomization was done by surgery room, not based on surgeon or patient.

2012 December 19

Kelly Green, Cardiology

  • Developing registry (~130 records) for two types of TAVR devices (aortic valve replacements); TAVR is fairly new - approximately 5000-6000 cases performed so far in US
  • Noticed large difference between percentage of white vs. African-American patients in registry
  • Screened 350 patients for procedure, only 15 were African-American, and only two taken for procedure
  • Prevalence of aortic stenosis among African-American population (or other minorities) is unknown
  • Looking to use BioVU, CMS (Medicare) data to compile registry - disease is almost exclusively in elderly patients; however, limited by diagnostic codes' accuracy and fact that population is pre-selected
  • One option could be to collaborate with Jackson heart study (~Framingham, recreated in Jackson, MS); might be better estimate of true prevalence, but this procedure isn't performed in Jackson
  • Main questions: What is the prevalence of aortic stenosis among African-Americans? Is there a racial disparity in how TAVR technology is applied, when adjusting for severity of disease, SES, etc?
  • These two questions require different data, different studies. #1 requires community-based survey.
  • National Cardiovascular Data Registry (NCDR) is in development, but not mature enough to use yet; Medicare Current Beneficiary Survey may be a good bet (age is available), but need enrollment file, ICD9s, procedures - need to get as close to entire population as possible, not just sick patients. Dave Penson at VUMC might be able to help with this.
  • Any major database like Medicare will take lots of time/power to work with
  • Applying for VICTR studio - no estimate of statistician time needed for that

2012 December 12

Scott Zuckerman (working with J Mocco); Cerebrovascular Surgery

Consultants: Sharon Phillips, Frank Harrell

  • Brain aneurysm surgical approaches
  • Coils can impact into the aneurysm. Is re-treatment needed? Open surgery needed?
  • Survey design - 30 people at a national meeting; hypothetical patients
  • May be 30 most experienced in the country; need to achieve nearly 100% response rate for the survey to be useful for the intended purpose
  • Can put confidence intervals around estimates
  • Think about either taking random sample of all possible permutations of patient characteristics or generate systematic combinations of char.
  • Each surgeon could get a different set of patient char. combinations
  • BUT Some combinations may never occur in nature; can use sampling from real cases to populate survey (a different random sample for each surgeon respondent)

2012 December 5

Alex Jahangir, Ortho/Trauma

  • Planning to submit for VICTR RFA
  • Looking at non-trauma joint replacement patients; ortho has highest incidence of rapid response calls, and 31% are from arthoplasties - perhaps due to age and comorbidities of patients?
  • VUMC has funded an NP to comanage - round on patients, manage meds, troubleshoot, facilitate discharge process; want to look at outcomes for a year before vs. a year after NP starts
  • Death, codes, PEs/DVTs, rapid responses, UTIs, length of stay, 30-day readmission, etc all considered potential outcomes along with costs
  • Collaborating with anesthesiology, ICU, finance
  • Suggest applying for mini-voucher from VICTR to help with grant preparation (Li Wang can instantly approve for <5 hours)
  • Eventually looking for manuscript; will need some sort of help with data cleaning (bioinformatics? biostats CSAs? student for data entry?)
  • Start prospective cohort a few months after NP starts to accommodate transition time
  • Account for double replacements or patients with >1 surgery during time frame?

Kaushik Mukherjee & Kendell Sowards, Trauma and Surgical Critical Care

  • Changes to a VICTR project - suggest talking to Li for specifics on biostats involvement with RFA
  • Three main aims: 1) does insulin resistance differ between patients who have diagnosed diabetes, occult diabetes, and stress hyperglycemia; 2) does insulin resistance differ between patients with and without ventilator-assisted pneumonia; 3) does knowing amount of insulin resistance help predict development of VAP over and above other diagnostic factors (x-ray, cultures, etc)
  • Recommend time-varying Cox model for time to VAP, adjusting for varying insulin drip amount or M (multiplier); other factors could also vary over time - medications, nutrition, etc, along with baseline factors (age)

2012 November 21

Chad R. Ritch, Fellow, Department of Urologic Surgery

  • Proposal development for a pilot study: need help for statistical analysis section
  • A pilot study of preoperative enteral supplementation (nutrient shake, similar to Ensure) before Radical Cystectomy (RC) surgery
  • Randomized ~150 cancer patients into 2 groups (intervention vs. control; all patients will get shake for 4-6 weeks after surgery; patients who previously got chemo excluded), primary outcome: number of complications per patient
  • Population averages about 25% patients with complications from surgery (about half of these have >1 complication); anticipate that intervention will decrease by 10%
  • Complications measured using Clavien scale
  • Potentially use albumin levels on day of surgery as primary outcome: main interest is complication rate or count, but with low N and low complication rate in the population, unlikely to have sufficient power to see a difference in dichotomous/few-category outcome; albumin is independent predictor of complications in previous studies
  • Should also collect data on feasibility for future grant application - how many shakes were drunk, etc (working with nutrition center on this)
  • Other potential secondary outcomes: time to first complication, length of stay after surgery, change in BMI or body composition/muscle mass (using dexa scan in nutrition core)
  • Albumin measurements: baseline, day of surgery (~4-6 weeks after baseline), day of hospital discharge post-surgery, 6 weeks and 90 days after surgery
  • Need to find mean and SD of albumin measurements in control population (mean around 3-3.5?); LeenaChoi can help with sample size
  • Also planning a weekly phone assessment of food intake from nutritionist
  • VICTR RFA deadline December 15

2012 November 14

Kaushik Mukherjee, MD, Division of Trauma & Surgical Critical Care

  • Has data from 2005-2011 on insulin resistance and blood glucose control for intubated patients. Stress-induced insulin resistance (IR) increases infection, including ventilator associated pneumonia (VAP). All vent patients are on an insulin drip.
  • Protocol for monitoring blood glucose (BG) has been in place since about 2002. There is a formula used to determine the insulin drip rate (IDR) = [BG(mg.dl)-60]xM. M is amount of adjustment for the drip to maintain a BG level between 80 and 110 (now using 100-130). When M is high, insulin resistance is increasing, so IDR goes up. The IDR is the best measure of IR.
  • The data consists of 314 patients with VAP and no other infection and 4098 patients with no infection. VAP is defined using CDC criteria, a patient has to be on the vent for 2 days before it is considered VAP.
  • The number of VAP infections peaks at about 5 days. After 10 days there is little information, so we would be looking at the time they were intubated to 10 days out.
  • They would like to know if there is a difference in IR between patients who are diagnosed with VAP and those who are not. Suggestions were to look at the change in IR and BG for all patients and to look at time to infection. They would need to adjust for nutrition,(i.e., glose drip, tube or TPN), mechanism of injury, age, comorbidities (a score used in trauma can be used for this).
  • Would like a voucher for biostats support, we estimated ~ 80 hours for this project.

2012 November 7

Moises Huaman, Fellow, Infectious Disease

  • Has a case control study with 4 groups. Cases are extrapulmonary TB, 11 patients
  • Three sets of controls: pulmonary TB, latent TB, and no TB, ~20 per group
  • Want to look at association between groups and vitamin D levels in blood.
  • Have done kruskal wallis and pairwise wilcoxon tests
  • Want to account for covariates, including season of vitamin D assessment, US/foreign born, age, sex, race, ethnicity
  • Recommend against excluding variables based on p-values
  • How to analyze residuals in stata?
  • Could use proportional odds ordinal logistic regression with vitamin D as the dependent variable. This is an extension of the kruskal wallis test. You don't want to group the outcome, vitamin D.
  • Also want to model tuberculosis and assess the impact of vitamin D, adjusting for other factors. You will have to prioritize the other covariates in this model. The number of parameters you can put in the model depends on the number in the smaller group.
  • Could use a propensity score to adjust for the propensity of TB adjusting for many other factors
  • Would like a victr voucher for biostatistics support. We estimate this will require about $3000.
  • Do not require the propensity score to be linear in the model.

Emily Reinke, Sports Medicine

  • http://biostat.mc.vanderbilt.edu/ClinStat/obsVar.pdf Has information about measuring agreement
  • Question about preferred method of establishing agreement. ICC or Bland-Altman? Frank prefers mean absolute discrepancy (within- or between-raters). Zhouwen has programmed R functions for this.
  • Patients who have undergone reconstruction are x-rayed to measure the space between the femur and the tibia of each knee on both the lateral and medial side and compare the reconstructed knee to the normal knee. We have imaged ~260 people of 420 people so far. The knee images are measured unpaired and blinded to outcome scores. Tell-tale hardware is hidden, but drilled tunnels cannot be hidden so blinding to a reconstruction is not complete. 28 pairs have been measured, 12 right knees additionally have been measured.
  • Initially, we had intended to use a single person to do the measurements. For various reasons we want to add a second reader. Before we did so, we wanted to establish that we had reliability between the two. Both readers measured 10 publically available images from the osteoarthritis initiative and then several months later did it again to determine the inter and intra rater reliability of the measurement method with these two readers. We are using Bland Altman to assess reliability. Note, previous evaluation of the reliability of the method has been performed using ICCs.
  • We had an unexpected finding. In our best case scenario, which came from the comparison of the experienced measurer with herself, the limits of agreement for the measurement[noise] and the greatest anticipated difference in the measurements between knees[signal] are roughly equivalent. In all other cases the limits of agreement are greater than the greatest anticipated difference in the measurements between knees. However, we are talking about tenths of millimeters in difference, and despite extremely similar bone orientation on the x-ray images- this is why I thought the publically available images could act as surrogates for the study images- the dissimilarity between the older arthritic knees in the publically available images and the young athletic ACL reconstructed knees in our study images are believed to be potentially sufficient to explain what we are seeing.
  • Is it permissible to take 10 pairs of images chosen at random from the 260 study images, have the two readers each measure them twice, perform Bland-Altman analysis on the measurements, and if the signal to noise ratio is reasonable, measure the whole group as if the 10 were never taken out?
  • We think using some of the actual data to assess the raters' reliability is fine, since it will be only a small subset and the raters are not likely to remember the images.
  • Recommend using more than 10, maybe 20.
  • Mean absolute discrepancy will be in the same units that you are working with, so that it is more interpretable. Measures degree of disagreement. You can get a confidence interval using bootstrap.
  • Bland Altman will show you the amount of discrepancy by the size of the measurement. It lets discrepancies in different directions cancel each other out.

2012 October 31

Frank Virgin, Otolaryngology

  • Working on a cystic fibrosis-related questionnaire study and needs help making sure that it is set up in a way that will allow analysis. Additionally, for the purposes of funding, I want to get a sense for the amount of statistical help I will need to analyze the results.
  • Discussed importance of getting a high response rate
  • Went over codings of particular questions, and advantages of truly continuous visual analog scales as implemented in REDCap survey
  • Bring back a revised questionnaire to discuss at clinic
  • VICTR $2000 voucher is likely to be adequate for analyzing the data

Curtis Baysinger, Anesthesiology

  • Dr. Baysinger is assessing agreement between two devices that assess effectiveness of a nerve block in cessarian delivery.
  • We encouraged Dr. Baysinger to contact Jonathan and Matt to arrange statistical support through an existing collaboration

2012 October 24

Kaushik Mukherjee, Surgery, Division of Trauma and Surgical Critical

  • attempting to determine if there is a temporal correlation between increased insulin resistance (as manifested by an increased insulin infusion rate) and the diagnosis of ventilator associated pneumonia in critically ill trauma patients.
  • Plans to apply for VICTR biostats support

2012 October 17

Irving Basanez, surgery resident

  • Looking at association of hearing loss and school performance
    • Inclusion: 11-12 years old, 492 total
    • Hearing measurement: each child was presented with a tone of 1000, 2000, 4000 Hz, and they were sounded with a volume of 30 dB. If they failed to hear a certain frequency, the volume would be increased to 35, 40, ... 80 dB. After the threshold is established, the next frequency was tried with 30dB. Resulting volume is the lowest volume in either ear.
    • Measurement of success in school: 4 exams (language, math, ...). Total score: # of correct questions divided by the # of total questions for all 4 tests together.
    • Patient characteristics: age, sex, grade, ear exam.
  • Analysis:
    • Linear (or proportional odds, depending on the distribution of the outcome) regression. Outcome: total test score. Covariates: age, grade, volume (at which the sound is heard). Might want to think about interaction term: age*volume (to allow the effect of hearing loss to be different for different age groups). Another possible interaction term: hearing exam * ear exam.
    • We recommend to choose the covariates a priori.
    • Check correlation of [age and grade] and [hearing exam and ear exam] , for example redundancy analysis.
    • Possible non-linear effect of hearing loss on test performance (might want to include hearing as a non-linear effect)
 

2012 October 10

Sunya Sweeny, orthodontic resident

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  • In digital teeth models, a bite registration helps show where the teeth fit together.
  • Wants to find the ideal material for bite registration. The comparison gold standard is the plaster model.
  • The articulation is going to be measured by measuring the distance between the top and bottom teeth in several different places. This will be done on the plaster model and with each material.
  • She is planning on using only one person's measurements. The variability comes from doing the measurements. * possible model: distance from physical ~ location + material + time OR
  • distance from physical ~material + time (with a separate model for each location)
  • Planning on doing about 25-30 measurements with each material.
 
  • Wants help with design and sample size
  • Topic is comparing the accuracy of interocclusal record materials in articulating digital dental models.
  • Associated literature has shown that laser scanned digital models are dimensionally accurate representations of plaster models. While plaster models can be easily and accurately hand articulated, articulating digital models is less accurate and time consuming, especially when anually or visually articulating them. An accurate interocclusal record could make the articulating process more efficient. No studies have looked at the accuracy different bite registration materials in mounting digital models.
  • Tentative materials and methods: 1 typodont mounted in maximum intercuspation on an articulator, 5 different bite registration materials.
  • Methods: Place 6 vertical interarch markers on each maxillary and mandibular arch (on the first molars, first premolars, and laterals). Control measurements between the interarch markers will be made using digital calipers on the typodont. Typodont will be scanned using Ortho Insight 3D laser scanner to create digital models. Bite registrations will be made on the articulated typodont using the five different types of bite registration materials. (Need to determine sample size of how many of each type of bite registration material needed). Bite registrations will be scanned using Ortho Insight 3D laser scanner to create digital bite registrations. Digital models will be articulated using the digital bite registrations. Experimental measurements will be made between the digital interarch markers using Ortho Insight 3D model viewing software. Results will compare the differences in the interarch measurements from the control group (physical typodont) and experimental groups (digital models articulated digital bite registrations).
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Sarah Hill, nursing doctoral student

  • Have an existing collaboration with Pediatric Department. The statistician is Ben Saville: b.saville@vanderbilt.edu
  • possible strategy: one model each for the number utilized during the operation and one model for number utilized post operatively.
  • number of units post op ~ weigh + post op lab values
  • note that the outcome, number of units, is count data. If using a regression model, appropriate options would be poisson regression or proportional odds ordinal logistic regression.
  • project for my DNP studies at VUSN.
  • retrospective chart review from 1/1/2010- 12/31/2011 for patients who have undergone craniofacial reconstruction for the treatment of craniosynostosis. I am specifically looking at the blood utilization with this encounter.
  • Research questions:
    • Is there a relationship between the patient's weight & total # RBC transfusions
    • Is there a relationship between the patient's weight & total # FFP transfusions
    • Is there a relationship between intraop Amicar use and # of transfusions given intraoperatively
    • Is there a relationship between intraop Amicar use and # of transfusions given postoperatively
    • Is there a relationship between the postop PCV and RBC transfusion administration at 6, 12, 18, 24, 36, & 48 hours postoperatively
    • Is there a relationship between the postop INR and FFP transfusion administration at 6, 12, 18, 24, 36, & 48 hours postoperatively
    • Is there a relationship between the total number of transfusions and length of PICU stay
    • Is there a relationship between the EBL (estimated blood loss) and amount of RBC transfusion intraoperatively
    • Is there a relationship between the EBL (estimated blood loss) and amount of FFP transfusion intraoperatively
  • Frank: "Please convert ages to all be in months and take away the "m" after the numbers."
 

2012 October 3

Thomas Abramo

  • Using near infrared to measure oxygen saturation in brain tissue. Interest in seeing if these measurements are predictive of a CT scan result.
Line: 20 to 243
 

2012 September 26

Emily Bullington, Pharmacy

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  • Project is ďPreventing and managing refeeding syndrome in the acutely ill: the importance of electrolyte replenishment.Ē
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  • Project is “Preventing and managing refeeding syndrome in the acutely ill: the importance of electrolyte replenishment.”
 

Gopi Shah, ENT research fellow

  • doing a project looking at the prevalence of hearing loss in the high schools here in Nashville.
  • would like to screen kids and then give them a survey about their perception of hearing loss.
Line: 45 to 268
 

Suseela Somarajan, General surgery

  • This is a study on finding the amplitude of gastric slow waves measured in human with different BMI before and after food.
  • For each BMI category, I have only 3 samples, have 9 total subjects.
Changed:
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  • Question: Is there any statistically significant difference in amplitude before and after food in each BMI category - performed Studentís t-test on the data (statistical significance set at p < 0.05). Since the sample size is very small the results are misleading. I need to show the data and discuss it.
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  • Question: Is there any statistically significant difference in amplitude before and after food in each BMI category - performed Student’s t-test on the data (statistical significance set at p < 0.05). Since the sample size is very small the results are misleading. I need to show the data and discuss it.
 
  • Gastric slow waves are measured serially. At each time point there are 16 measurements. They take averages at each time point. Before the meal, there are eight observations, and eight observations after the meal.
  • For the question about the amplitude of the waves, you should adjust for BMI. Since there are many time points within each person, the data are correlated.
  • Since the sample size is small, focus on graphical presentation. Could plot each person's trend, with time on x amplitude on y, and show BMI with color. Indicate the time of the meal with a vertical line.
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Revision 105
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META TOPICPARENT name="ClinicSurg"

Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

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2012 October 10

Sunya Sweeny, orthodontic resident

  • Wants help with design and sample size
  • Topic is comparing the accuracy of interocclusal record materials in articulating digital dental models.
  • Associated literature has shown that laser scanned digital models are dimensionally accurate representations of plaster models. While plaster models can be easily and accurately hand articulated, articulating digital models is less accurate and time consuming, especially when anually or visually articulating them. An accurate interocclusal record could make the articulating process more efficient. No studies have looked at the accuracy different bite registration materials in mounting digital models.
  • Tentative materials and methods: 1 typodont mounted in maximum intercuspation on an articulator, 5 different bite registration materials.
  • Methods: Place 6 vertical interarch markers on each maxillary and mandibular arch (on the first molars, first premolars, and laterals). Control measurements between the interarch markers will be made using digital calipers on the typodont. Typodont will be scanned using Ortho Insight 3D laser scanner to create digital models. Bite registrations will be made on the articulated typodont using the five different types of bite registration materials. (Need to determine sample size of how many of each type of bite registration material needed). Bite registrations will be scanned using Ortho Insight 3D laser scanner to create digital bite registrations. Digital models will be articulated using the digital bite registrations. Experimental measurements will be made between the digital interarch markers using Ortho Insight 3D model viewing software. Results will compare the differences in the interarch measurements from the control group (physical typodont) and experimental groups (digital models articulated digital bite registrations).

2012 October 3

Thomas Abramo

  • Using near infrared to measure oxygen saturation in brain tissue. Interest in seeing if these measurements are predictive of a CT scan result.
  • The measurements are taken over time, and the hypothesis is that a big difference in the two hemispheres or overall low values are predictive of pathological CT results.
  • One idea is to have blinded clinicians rate the near infrared graphs as pathological (yes/no), and then look at the predictive ability of the ratings of the CT result.
  • Another approach is to get a summary measure. The summary measure could be the variance in each side.

2012 September 26

Emily Bullington, Pharmacy

  • Project is ďPreventing and managing refeeding syndrome in the acutely ill: the importance of electrolyte replenishment.Ē

Gopi Shah, ENT research fellow

  • doing a project looking at the prevalence of hearing loss in the high schools here in Nashville.
  • would like to screen kids and then give them a survey about their perception of hearing loss.
  • question is how to figure out the number needed in order to give the study power.
  • Frank: "This is not a power problem but a precision (margin of error) problem. My approach to this is to solve for N such that the 95% confidence interval for a probability is +-z when the probability is at the worst case (0.5). z might be 0.1. Statisticians at that clinic can help and can discuss this further to make sure what are your needs."

2012 September 19

Juliana Kyle and Susan Hamblin, Pharmacy

  • Protocol for open fractures (bone pierces skin)
  • Main compliance issue is antibiotic duration. There is also an antibiotic rotation. Also a physician could be noncompliant by not prescribing any antibiotics at all. Right now the group is interested in a composite yes/no overall compliant.
  • Want to assess compliance and its relation to outcomes in terms in drug resistance
  • Primary outcomes are infection and resistant infections before discharge.
  • Secondary endpoints are mortality, length of stay, icu length of stay.
  • Have about 200 patients. Rate of infection may be around 50%. Resistant infection is about 20-70% of those, based on literature.
  • Factors that may affect infection: length of stay
  • Analysis strategy: logistic regression with types of compliance, patient factors (diabetes), physician factors, length of stay
  • Limitations: have know way of knowing each patient's prior exposure to resistant organisms
  • May want to consider other sites besides the original site.
  • It would be useful to collect time to infection, but need to consider the uniformity of assessment. May use time to first positive culture.
  • Many issues, better to have a statistician on board. For purposes of a VICTR voucher, we estimate that this would take roughly 50 hours.

Suseela Somarajan, General surgery

  • This is a study on finding the amplitude of gastric slow waves measured in human with different BMI before and after food.
  • For each BMI category, I have only 3 samples, have 9 total subjects.
  • Question: Is there any statistically significant difference in amplitude before and after food in each BMI category - performed Studentís t-test on the data (statistical significance set at p < 0.05). Since the sample size is very small the results are misleading. I need to show the data and discuss it.
  • Gastric slow waves are measured serially. At each time point there are 16 measurements. They take averages at each time point. Before the meal, there are eight observations, and eight observations after the meal.
  • For the question about the amplitude of the waves, you should adjust for BMI. Since there are many time points within each person, the data are correlated.
  • Since the sample size is small, focus on graphical presentation. Could plot each person's trend, with time on x amplitude on y, and show BMI with color. Indicate the time of the meal with a vertical line.
  • If you collect more patients, focus on modeling, maybe with the difference in pre- post activity as the dependent variable. Could also use longitudinal data, but would need to account for correlation within patients, maybe with random effects.

2012 September 12

Jenny Rothchild, Urology fellow

  • Recommended that she plot histograms of each count or continuous outcome (in aggregate) to assess the distribution
  • Recommend controlling for physician variables or physician in a model
  • May need help in analysis, so recommend considering applying statistical support through a VICTR voucher
  • Results of the histograms will help us to recommend models.

Eric Thomassee, Cardiology fellow

  • Would like feedback on the datapoints prior to data collection. The project involves acute pulmonary emboli and all assoicated therapies (surgical, percutaneous, and medical).
  • We looked at his redcap database and made recommendations.
  • We emphasized collecting actual dates rather than, for example, length of stay or three month mortality (yes/no).
  • Found some instances where radio buttons were more appropriate than check boxes, when the categories were mutually exclusive.

2012 August 15

Daniel MuŮoz, Fellow, Cardiovascular Medicine

Background: Patients who are admitted to ED with chest pain are usually given EKG, troponin test and assessed for a cardiovascular risk score. If all these are within acceptable ranges, they are often admitted for stress test or observed in ED, both of which use lots of resources. Rates of MI/CV death within a month, given the patient has acceptable EKG/troponin, are extremely low. Planned "intervention" is discharge to outpatient with stress test within 72 hours, vs. "control" of typical ED/hospital observation. Planned outcome: major CV event within 30 days (CV [or not clearly non-CV] death, MI). Event rate is hypothesized to be roughly the same for both groups at about 1%. Will also collect resource data, like costs.

Potential issues:
  • Very low event rate -> very large sample size required. Possibly include hospitalization for chest pain and/or revascularization as part of "CV event" outcome.
  • Compliance/patient selection - patients would be responsible for getting their stress tests. Could select patients based on insurance or other factors, but that would limit generalizability and possibly create selection bias.
  • Followup: Planning on SSDI for death, phone followup for other event(s).
  • Secondary outcomes: repeat ED visits
My specific question: what might be the approximate target sample size for a potential trial of the following design?
  • 1:1 randomization of low risk patients into one of two treatment arms (ED eval vs Outpatient eval) in which we aim to test a hypothesis about non-inferiority of outpatient eval as compared with current standard treatment (ED eval)
  • Primary endpoint is a dichotomous composite endpoint (Death/MI or no Death/MI at 30 days) with an anticipated event rate of approximately 1% in each arm
  • Level of significance 0.05
  • Desired statistical power = 0.90
To use the most commonly used approach (relative efficacy using odds ratios) can be used as follows. The standard error of the log odds ratio is the square root of twice 1/[np(1-p)] where p = 0.01. The upper confidence limit of the log odds ratio will be log observed odds ratio + 1.96 times this standard error. Setting 1.96 se to log(1.25) if the multiplicative margin of error is allowed to be 1.25 yields n = 2*((1.96/log(1.25))^2)/(p*(1-p)) = 15,586 patients in each group for a total of over 31,000 patients. The large n is the result of the tiny incidence rate.
<-- 
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 p &lt;- .01 2*((1.96/log(1.25))^2)/(p*(1-p)) 
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2012 August 08

General Surgery, Pediatric Surgery: Brian Bridges, Michael Northrop

  • Examine the effect of new laboratory procedure on the outcome of pediatric ECMO
  • Control: patients in the previous year; Treatment: patients in the current year receiving the new procedure (N=80 each group)
  • The new procedure is to monitor the activity of heparin
  • Outcome: survival to hospital discharge, time to bleed, (heparin and other parameters)

2012 July 18

General Surgery, Pediatric Surgery: Andre Marshall

  • Congenital Diaphramatic Hernia
  • Does placing chest-tube during repair improve mortality at 6 months.
    • Does not have date that mortality was assessed.
    • Recommend collecting date of death, or at least verifying that mortality assessments are made after 6 months (or 30 days).
  • Apply for VICTR support in the amount of $3500

2012 July 11

MPB: Kate Ellacott

  • Main analysis: the effect of weight loss on a biomarker.
  • Population: patients who had a gastric bypass surgery.
  • Main question: would like to get power calculation of how many subjects are needed to detect a difference in biomarker level before and after surgery.
  • Recommendations:
    • define a clinically meaningful difference in the biomarker from the previous literature (instead of putting a difference that you observed in the previous study)
    • main analysis should be based on the same test that the power calculation was done (probably paired t-test)
    • if the meaningful difference is not know, might be a good idea to estimate possible number of subjects that can be collected and calculate power for difference that can be detected in that many subjects

MPB: Ember Sympson

  • Outcome: post liver volume
  • regression covariates: planned volume, day from surgery,
  • get estimate and confidence intervals for coefficients for each of the covariates

Anesthesiology, Paul W. Hannam

  • Looking at how well some three scores predict certain outcome
  • Outcome: morbidity defined as renal failure, organ failure, or death
  • Recommendation:
    • logistic regression with outcome: 1 - had an outcome within 30 days, 0 didn't have an outcome within 30 days
    • run separate regression for each score
    • covariates: ????
    • compute predicted probabilities of outcome, and Brier index

2012 June 20

General/Pediatric Surgery: Andre Marshall, Martin Blakely

  • Looking at adverse event rates among appendectomy patients from two separate trials - difference between two treatments?
  • Interested in funnel/forest plots, getting overall ORs and AE-specific rates
  • Planning to do formal meta-analysis? Need input from CTSA statisticians in terms of number of hours
  • Would a mixed effects model adjusting for trial be sufficient, rather than formal meta-analysis?

Plastic Surgery: Joshua Anthony

  • Data on hand surgery calls from 111 hospitals in TN capable of handling hand emergencies
  • Do availability for hand call, other outcomes differ for medically underserved areas vs. others, or teaching vs. non-teaching hospitals?
  • For outcome of hand surgery availability, recommend logistic regression model: hand call = medically underserved + teaching hospital + urban area (all yes/no variables); similar for other outcomes like hand surgeon availability
  • Very sparse data for level 2/3 trauma centers (1 and 2 hospitals respectively); recommend combining with level 4 trauma centers to compare level 1 (surgeon should be available 24/7) vs. everyone else
  • 20 hours of VICTR help is probably sufficient for a few logistic models, explanations, manuscript edits (emphasized that clean data is extremely helpful in keeping time down!)

2012 April 25

Anesthesiology : Damon Michaels, Jennifer Morse, Lesley Lirette

  • General:
    • Observational study
    • 2 months
    • two groups: residents, attending physician performing regional anesthesia (nerve block)
    • group sizes: 131 (attending physician) 150 (residents)
    • there are repeated measures: there are only (6 residents+one fellow), and 7 attendings. And for each procedure different attendings supervised different residents.
    • it was not recorded what resident/attending did what procedure.
  • Outcome: delay = time when the patient actually arrives into OR minus time when the OR is ready for the patient. Originally wanted to dichotomise the outcome into 5 minutes or less and more than five minutes.
  • Hypothesis: there is no delay
  • Suggestions:
    • keep the outcome continuous
    • if possible, recover the information what resident/attending did what procedure, and put everything into one model
    • or leave it as is and state in the limitations the fact that there are repeated measurements
    • Recommend you look at the data graphically. You can learn both about whether there was a delay and about the distribution of the times. Do two boxplots (one for residents and one for attendings) overlayed with the actual data.

Medical student group: Monika Jering

  • General:
    • looking at the adult APGAR (score based on blood loss) per minute
    • general question: if a drastic change in the score affects mortality (morbidity)
  • Population: 400,000 admissions.
  • Outcome: death or complication within 30 days
  • Hypothesis: A "sharp" drop in the score is associated with higher risk of death (or complications)
  • Suggestions:
    • define a drop as a maximum drop within one minute period
    • For simplicity: use first admission for each patient
    • adjust for baseline, age, length of surgery, comorbidity index, type of surgery, and other mortality

2012 April 11

Medical student group: Beth Greer, Michael Maggart, Neelam Patel, Calvin Sheng, Tenisha James, Cooper Lloyd

  • Children are classified as exceptionally behaviorally inhibitted or uninhibited, as assessed by a questionnaire
  • Children are given an MRI to measure the amygdala size in volume by summing cross sections. There is specific protocol. The person measuring the images will be blinded to the behavioral status.
  • Could do a separate study to assess intra-rater reliability (have the person assessing the images rate some of the images twice in a blinded fashion).
  • May be a benefit to including patients who are more in the middle of the spectrum
  • There is probably a selection bias in the sample of patients who participate in the study, due to the mechanism of accrual.
  • There are other assessments given to exclude patients with other disorders, including autism.
  • The group wants to know about sample size
  • They will supplement their data with data from a separate database. Recommend that a random procedure be used to draw a sample from this database
  • Research question is whether there is a difference in amygdala volume in the two groups.
  • To find the sample size needed, would need to think about what the smallest clinically meaningful difference in amygdala volume would be and also the standard deviation of the amygdala size in the control group.
  • Another way to look at this research question is to use the inhibition score without categorizing, and testing for association with amygdala size. Depending on the way the score is assessed, this could have the advantage of preserving statistical power.
  • Can use the PS software by Bill Dupont. Free download.
  • Ideally would want to use the amygdala as the outcome variable and adjust for the total brain size as well as inhibition score.
  • The amygdala size is hypothesized to influence the behavior score. If you keep the behavior as two groups (inhibited and uninhibited), the appropriate analysis would be a logistic regression predicting the odds of being in the inhibited group.

2012 April 4

John Newman, Voranaddha Vacharathit SOM

  • Research question: Does animation improves comprehension.
  • Hypothesis: Animation improves learning
  • Outcome:
  • We recommend: linear regression with the post-test score as outcome and four covariates: pre-test score, animation (1=yes, 0=no), style (1=visual, 0=experimental), interaction of animation with style (animation*style). The interaction term allows to model different effect size for different styles .

Katie Guess, Stessie Dort, Fayrisa Greenwald, VUSM

  • Population: trauma coordinators in trauma ICU. The researcher are planning to send a survey to 219 trauma centers.
  • Research question: Whether trauma coordinators assess and educate about acute and post traumatic stress disorder.
  • Concerned about: low response rate, the lower response rate the higher the bias might be.
  • Recommendation: set questions in a way that you don't have to categorise. RedCAP allows to have a scroll bar, where you can mark a percent between 0 and 100. Continuous measurements have more power.
  • Output: descriptive statistics for each question of the survey. For yes/no or categorical questions we report to report proportion (out of the whole population in each region), for ordinal or continuous variables we recommend to report medians and inter quartile range. To compare responses between regions we recommend Pearson chi-square test for binary or categorical variables, and Wilcoxon Rank Sum test for ordinal or continuous variables.

Rich Latuska, VUSM

  • Study question: Do patients requiring multiple specialties for care (ortho, neuro, etc) have better/worse comprehension of their injuries and satisfaction with their care?
  • Likely prospective study enrolling trauma patients, with survey given at first clinic visit and followup (3-6 months)
  • To determine required sample size, talk with mentor about what detectable difference would be important (5% difference in patients satisfied vs. 25%...) and how many patients it's reasonable to survey over available time period
  • Keep instrument mostly the same at both time points, likely with some additional questions at long-term followup

2012 March 28

Kevin Carr, Candidate for MD degree

* See attached files

 

2012 March 21

Michelle Huber, Pharmacy

  • Project is titled: Delirium and pain in the post operative cardiac surgery patient: a retrospective review. IRB: 111619, PI: Chad Wagner, MD.
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  • How to analyze this data for one month? (we can do something very simple, keeping in mind analysis will be on a larger scale once I have all data)
  • The cost to do a simple analysis for the one month of data so that I can have something to present in April?
  • For the whole project of 5 months, how to analyze this data? Need to send an estimate for VICTR funding.
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  • Already have data for 84 patients.
  • Retrospective chart review of patients that had cardio surgery and were transferred to CVICU.
  • In first 48 hours after surgery. Looking at pain and CAM scores (delirious vs. not.). Have data on all drugs.
  • Want to see whether uncontrolled pain is a predictor for delirium.
  • One thing to account for is people who wake up from surgery already delirious. In this case, it will be impossible to assess pain.
  • Is there a possibility of working with Jennifer and Amy, who have experience in working with delirium. This would need to be billed through the BCC. Contact Ayumi Shintani to discuss this possibility. ayumi.shintani@vanderbilt.edu
  • Need to define uncontrolled pain. Could leave this variable as ordinal.
  • Probably for the April 20 presentation, given the time constraints and current number of patients, probably a more simple analysis would be appropriate. Expect ~18-28% events, which would limit the complexity of the analysis or model. Use graphs to display the data.
  • We estimate that this project would take about 45 hours over the next year or so with some effort within the next month for the presentation in the end in April, so an estimate of the cost is $4500.

Stan Pelosi, ENT

  • Descriptive study of pediatric patients with auditory neuropathy who get cochlear implants (inner ear implant for deafness)
  • Auditory neuropathy involves a functioning cochlea, but they are still unable to hear.
  • Risk factors for auditory neuropathy from literature:
  • Initial goal is to describe the patient population.
  • Need help in interpreting a previous analysis
  • The output in stata is a an estimate of the proportions of some variables in the data and also a test of whether the proportions were equal. For example, they tested whether the proportion of premature patients was equal to the proportion of non-premature patients.
 

2012 March 14

Shilo Anders

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Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

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2012 March 21

Michelle Huber, Pharmacy

  • Project is titled: Delirium and pain in the post operative cardiac surgery patient: a retrospective review. IRB: 111619, PI: Chad Wagner, MD.
  • Have 1 month of patient information for my project with hopes to receive 4 additional months for a total of 5 months. I have already data collected (everything is in RedCap) for that first month of data. As a resident I am required to present my research project at a meeting in April. I will not be able to data collect the rest of the patients in time for this presentation but I do want to analyze what I have for this meeting.
  • How to analyze this data for one month? (we can do something very simple, keeping in mind analysis will be on a larger scale once I have all data)
  • The cost to do a simple analysis for the one month of data so that I can have something to present in April?
  • For the whole project of 5 months, how to analyze this data? Need to send an estimate for VICTR funding.

2012 March 14

Shilo Anders

  • Wants to address reviewer comments:
There should be analysis for trend used for the analysis rather  
than multiple tests with Bonferroni adjustments.

The authors state that less time was devoted to instability and error  
detection.  However, the total number of interventions was  
significantly larger in the 2007 observation period.  Thus the total  
number of instability and error detection events may have remained  
constant, while the team significantly expanded their role in other  
areas.  The nature of the interventions that increased in frequency in  
2007 might well reflect improved acceptance of the tele-ICU system by  
the bedside providers, with better collaboration and expanded requests  
for assistance.  I believe that the manuscript could be improved if  
the authors expanded their analysis of the nature and possible causes  
of the observed distributional changes in tele-ICU interventions.

The minor revision required is to provide statistical evidence on data  
reliability and data validity.

2012 March 7

Dave Janz, Critical Care

  • We are designing a trial of using acetaminophen to treat sepsis. Before conducting a large trial looking at clinical outcomes, we are going to do a smaller one just looking at changes in markers of disease after 2 days of acetaminophen therapy compared to prior to starting therapy. My specific question is in regards to how to power the study to be able to detect these changes. We will be studying isoprostane levels and the mean level in this population is 65.7 pg/ml, SD 67.4 (isoprostanes are not normally distributed in humans). We would like to be able to detect a change of 20 pg/ml in those receiving acetaminophen and also to be able to detect a difference of 20 pg/ml compared to a group that receives no acetaminophen.
  • We are planning a unrelated study trying to determine the reason why patients in the ICU who receive red blood cell transfusions are more likely to die than similar patients who are not transfused. Specifically, it will be an observational study that will measure 21 markers in the blood of transfused patients immediately before transfusion, along with 4 hours after and 24 hours after. There will be no control group. These markers are also not normally distributed in humans and the marker with the largest standard deviation is IL-8 with a mean of 457.6, SD 7641.4. What I would like to be able to detect a change of half a standard deviation in this group. Do I need to account for multiplicity?
  • Recommend getting sample sizes for the different possibilities of the standard deviations of the difference in pre-post drug outcome. 65 may be the upper bound on the standard deviation.

Tom Golper, Nephrology, Department of Medicine.

  • Need a randomization scheme. Two groups. The experimental condition is the vehicle
  • Recommend a randomized permuted block design. with varying block sizes.
  • Want to do an interim analysis based on the outcome during which a decision will be made to terminate early for efficacy or futility. We recommend from the description of the need for an interim analysis, that an interim analysis based on the outcome is not needed. We recommend that after, say, 40 endpoints are observed, the standard deviation be reassessed, and the sample size be re-calculated.
  • Account for the number of patients who die.
  • Must account for the baseline values in the analysis, since someone with a very low level to begin with has less room to decrease.
  • They assume that about one fourth of the patients will have filters that last 70 hours, when the patients will automatically have their filter changed. Thus, there will not be an observed failure time for those patients.
  • There is a censoring issue, both from lasting more than 70 hours, and also people dying.
  • Want to show that the syringe is not inferior to the infusion.
  • JoAnn Alvarez will help with the permuted blocks.

2012 February 29

Steven White, Emergency Medicine

  • Study of an informatics tool to automate access to the state of tennessee prescription monitoring program (PMP).
  • The tool is much faster than the existing tool
  • The primary study objective is to determine whether automated access results in a higher proportion of ED patients being screened for controlled substance misuse.
  • Additional objectives will be whether increased screening results in a decrease of opiate prescriptions at discharge and whether there are any PMP report variables which influence decision to prescribe opiate at discharge.
  • The study population involves all patients discharged from the ED over a two-month period, with tool ON/OFF at two week intervals. During tool OFF periods, clinicians still have access to the PMP using the web browser.
  • The following data is collected from each PMP automated query: number of prescriptions for controlled substances previous 12 months, number of different prescribers, number of different pharmacies, number of opiate prescriptions, number of days since most recent prescription, pain score at triage, one-way encrypted case number, one-way encrypted MRN, one-way encrypted user racfid, data viewed flag, file opened flag. For periods during which the automated tool is OFF, we will obtain only counts of queries without specific patient data.
  • From ED discharge instruction summary (generated from discharge instruction writer), for each patient discharged during both tool ON and OFF intervals, we will have flags for patient-volunteered opiate use at home, flag for opiate prescription at discharge, name and quantity of product prescribed, one-way encrypted case number which will match up with encrypted case number from automated tool query, encrypted racfid of discharge attending.
  • Could analyze this at the doctor level, and have repeated measures. For example, each row in the data could correspond to one shift. You would collect the number of patients the doctor accessed in each of the two systems, and the number of opiate prescriptions the physician prescribed in that interval.
  • We have an existing collaboration with the Emergency Department. See Cathy Jenkins. We think Jonathan Schildcrout works with DBMI.

Andre Marshall, General Surgery

  • See attached document
  • Wants to study readmission in pediatric surgery with retrospective data. Want to see which diagnoses and procedures predict readmission within 30 days.
  • A big question is whether the readmission was caused by the procedure or not. An algorithm could exist that payers use. If you could use that same algorithm in your study, it would be much more objective than having a group in your team decide each one.
  • Jonathan Schildcrout is working on outcomes research.
  • Recommend putting patients that did not get readmitted.
  • If you can't enter all the "control" data into redcap, you need to randomly select them.
  • If you go through VICTR, we estimate that it will take about 40 hours, or about 4000 dollars.

2012 February 22

Mei Liu, Biomedical Informatics

  • Retrospective study. Outcome: Abnormal lab test. Exposure: Certain medication
  • Planning to do propensity score matching to compare each medication group to a control group.
  • Recommended to talk to Michael Matheny (maybe to Jonathan as well)

2012 February 15

Michelle Collins, School of Nursing, and Sarah Star, OB Anesthesiology

  • Planning a retrospective chart review of patients who got nitrous oxide in labor.
  • What patient factors influence success of the the nitrous oxide.
  • Outcome is whether the patient got an epidural.
  • Have about 350 patients over about one year period
  • They think parity, use of oxytocin induction, oxytocin augmentation, length of labor, provider type (midwife, ob, combo). May want to adjust for time since nitrous oxide was made available in the hospital.
  • Nitrous oxide relieves anxiety. Some women may want nitrous oxide but not an epidural because they can more effectively push, and because they have more control on the amount
  • If you include patients that did not get nitrous oxide in the first place, it may be important to include a propensity score for using nitrous oxide.
  • Can email Jonathan Schildcrout to see if there is already a collaboration in place. jonathan.schildcrout@vanderbilt.edu

2012 February 8

Pat Keegan, Urologic Oncology

  • Planning a trial to investigate differences in using staples vs. ties to close blood vessels in prostatectomy.
  • The outcomes they want to compare by are surgical margins and continence.
  • The investigator had already made some sample size calculations in PS and wanted to review them with us.
  • We recommended accounting for attrition in the sample size
  • We recommended collecting the actual measurement of the margins rather than only whether they were positive, if feasible.
  • Recommend collecting the patient-reported number of pads used per day rather than only collecting three levels: 0-1, 2-5, 5+. The number of pads can always be grouped later if necessary, but if you only collect the three groups, there will be no way to obtain the actual value.

2012 February 1

Blake Hooper (Anesthesiology)

  • Evaluate two new cath methods compared to gold standard; cardiac ouput as outcome
  • 15 subjects, A was performed on all 15 subjects, B was performed on 5 subjects, gold standard from all the subjects
  • measurements of different methods were made at the same occasions for each subjects; the number of measurements ranged from 8 to 12 for the subjects.
  • Expect the diff between methods and gold standards change with time; non-monotonic trend is expected.
  • Mixed effects model
  • Apply for a Voucher $2000 (http://www.mc.vanderbilt.edu/victr/pub/message.html?message_id=139)

2012 January 25

Lucy He (Neurosurgery)

  • Looking for risk factors for Grade 1 vs. Grade 2 meningioma (diagnosed via radiograph)
  • Outcome is Grade 1 vs. Grade 2 tumor; currently have 128 patients, 94 Grade 1 and 34 Grade 2
  • Limits degrees of freedom possible for well fit multivariable regression
  • A priori chosen potential risk factors include edema (four categories), necrosis (yes/no), and location (four quadrants)
  • Eventual goal is prediction model
  • For now, getting more data probably not practical
  • Suggested VICTR application; either logistic regression (possibly using just edema and necrosis - 3-4 df max), or possibly using propensity scores or other data reduction techniques

2012 January 18

Todd Morgan

  • Looking at patients with metastatic kidney cancer who have undergone nephrectomy.
  • Want to see if the tumor pathology data predicts survival. Also lab values. The want to adjust for type of chemotherapy.
  • They have run a Cox regression
  • They want to create a nomogram
  • Want to look at the predictive ability of the model. Suggest internal validation using bootstrap.
  • Also want to validate a prior model that only uses preoperative data.
  • Have 63 events out of 88 records.
  • Suggest adding the post-op data to the pre-op model and assessing the contribution of the additional variables.
  • Discussed ways to account for chemotherapy in the model yet adequately capture the difference between different chemotherapy regimens. All of the current therapies are similarly not very effective. They aren't interested in quantifying this effect; they want to control for it.
  • In validation, also need to validate the model selection process.
  • Need to check the prop hazards assumptions.
  • Recommend at least 40 hours to complete this project.

2012 January 11

Stephen Kappa

  • Looking at cost in cystectomy in bladder cancer, comparing laproscopic vs. traditional surgery.
  • Recommended that all dollar amounts are adjusted to one standard across years using the consumer price index.
  • Talked about approaches for getting biostat support: Check with Tatsuki Koyama to see if this project is covered under existing grants. Another option is a VICTR grant.

Eric Gehrie, pathology resident

  • Looking at abo incompatibility in heart transplant
  • Hypothesis is that poor outcomes (death within a month) are correlated with amount of incompatible antigen in the transplant heart.
  • Have data from 18 cadavers.
  • Has tested amount of antigen in the hearts using two ways: one is tissue staining and categorizing into 3 levels by a pathologist; the other is scoring by a machine the percent positive.
  • Wants to see whether the amount of antigen is associated with factors like gender, BMI, and time between death and autopsy.
  • Recommend displaying all the data graphically.
  • To look at the variability, make a strip plot of the percent positive variable. This will show how variable the data is compared with the median.

2012 January 4

Kassatihun Gebre-Amlack, School of Medicine and Department of Anesthiology

  • Want to understand characteristics associated with transfer to ICU.
    • Outcome is transfer to ICU for any surgical procedure (non-thorasic).
    • 763 required transfer from floor. 63,000 did not require transfer from floor.
  • Predictors of interest include: past medical history (diabetes, cad, cholesterol, renal function), demographics, surgical procedure (length, medicine, heart rate, rescue).
    • Consider logistic regression model conditioning on all covariates to model probability of transfer to ICU -- single (1 predictor) and multivariable (>1 predictor)
    • Surgeries with longer hospital stays may see more transfer to ICU, will include length of stay.
    • Five years of data, have protocols for transfer to ICU changed? Consider including date of procedure to adjust for this effect.
  • Could consider a combined endpoint of death/transfer to ICU to identify patients at risk.

2011 November 30

Rebecca Snyder, Department of Surgery; Martin Blakely is the mentor

  • Wants to estimate the agreement between three raters of an ordinal scale. There are four levels.
  • Also want to know if the agreement varies for different types of surgery.
  • Recommend using a weighted kappa for the measurement of agreement.
  • For the different types of surgery, they could get separate kappas for the different surgery types (depending on sample size within the types) or do a logistic regression predicting log odds of at least one disagreement between the three raters, including type of surgery as a predictor.
  • Wants to know the sample size required. Recommend estimating sample size based on the precision of the kappa statistic.
  • Estimate that a $2500 VICTR Biostatistical support request should be sufficient

2011 November 16

Robert Kelly, General Surgery, Bariatric DIvision

  • Wants to look at agreement between biopsies taken from different sections of the liver in surgery patients.
  • Each of the biopsies get an ordinal score with 6 levels (majority are in 4 categories)
  • Two sections will be taken from each patient.
  • We recommend that the evaluator of the specimens be completely blinded to the location of the specimen and also the identity of the patient.
  • Wants a refined estimate of number of samples they need.
  • Think about what difference in measurement would be clinically significant.
  • Could calculate sample size required to estimate a weighted Kappa statistic with a 95% confidence interval of a certain width.

2011 November 9

Nick Burjek, Ryan Hollenbeck

  • Retrospective study of 150 patients
  • Outcome variable is good vs. bad neurological outcomes: Outcome comes in a 1-5 ordinal scale: CPC scale at ICU discharge
  • Want to assess prognostic value of bispectral index, which measures brain function and also amount of sedation
  • They have many patients who are on both fentanyl and versed
  • One option for the measurement of bis is time under level of 40.
  • Probably need to incorporate an interaction between bis and sedation requirement.
  • Discussed different ways to summarize the predictor variables.
  • Possible summaries could be slope, intercept (where the started), final level, area under the curve, minimum, time less than 40. (Some of these could be used for either sedation of bis.)
  • Encourage to look at plots of raw bis and sedation data over time for each patient.
  • Can evaluate the effects of many variables in one regression model
  • Want to apply for VICTR statistics support. We estimate that $3500. OR, they may have access to Jonathan's group through an existing collaboration.

Khensani Marolen, Anesthesiology

  • Prospective study on patients going in for surgery. Collecting data on all drugs the patient is on.
  • Two sources of data: one the patient enters at home, and the other is at a clinic visit
  • 160 patients have both sources of data.
  • Want to test the hypothesis that the patient-entered data is more accurate and has more detail and more drugs. We have data that can answer how the responses agree and which source has more drugs, on average
  • Right now excluding OTC and vitamins.
  • Wilcoxon signed rank test and confidence interval
  • Be sure to include limitations of this analysis: assumption that all drugs listed are actually taken by the patient
  • Can interpret point estimate with confidence interval as the difference in medians between the two groups
  • Could also check the actual concordance between the lists
  • The patient self-entry device also collects info on comorbidities
  • Want to increase enrolment mid-study by offering a gift card
 
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2011 Roctober 26

 

Elizabeth Card, Anesthesiology

  • Want to look at the long-term effects of hypoxia.
  • Have two groups of about 650.
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    • 1-6 ordinal (1=healthy to 6=extremely sick).
    • Want to assess the differences between two reviewers in one modality
    • suggest (weighted) Kappa -- agreement between reviewers for a given patient - report confidence intervals {http://www2.sas.com/proceedings/sugi22/STATS/PAPER295.PDF}
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    • paired Wilcoxon sign rank - test for difference in distribution of scores between reviewers

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library(irr)
kappa2(r1[,c("ASA_Score_Experimental","ASA_Score_Control")], weight="equal") 
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<-- 
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library(irr) kappa2(r1[,c("ASA_Score_Experimental","ASA_Score_Control")], weight="equal") 
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2011 Sep 28

Paul Moore, Susan Hamblin, Pharmacy
Consultants: Frank Harrell, Svetlana Eden, Mario Davidson, JoAnn Alvarez

  • Reducing atrial arrhythmias esp. fibrillation, in trauma patients; role of anti-oxidant therapy (AO)
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META TOPICPARENT name="ClinicSurg"

Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

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--++ 2011 Roctober 26

Elizabeth Card, Anesthesiology

  • Want to look at the long-term effects of hypoxia.
  • Have two groups of about 650.
  • Designing a follow-up study to a randomized trial in which all patients were were monitored constantly for blood oxygen saturation levels, and those in the treatment arm had the monitor data monitored in real time
  • One of the aims is to look at the hypoxia that was detected by the stealth monitor, versus the the hypoxia which was detected by the standard of care, which disturbs the patient.
  • Discussed whether the outcome of interest is the sum of time in a hypoxic state.
  • One main outcome is whether the patient was readmitted to the hospital for any reason within 30 days.
  • One other confounder that could be adjusted for or used as an offset or as a predictor is the length of time that they were on the stealth monitor. We think that the differences in the time on the monitor is independent of the patient's health status.
  • Length of stay should be controlled for in models with hypoxia as an outcome. Also amount of blood loss and sedation medicine. (And everything else that is known to affect readmission for the readmission model)
  • One way to measure the hypoxia is the average amount below the lower limit for health. This is the area under the limit divided by the time monitored.
  • Look at the correlation of the nurses' oxygen saturation measures with the stealth monitor's measures.
  • Could also consider the minimum oxygen saturation reached during the observation period. This could be used as a predictor for the patient health outcomes (like readmission) along with the average amount below the lower limit described above.
  • Could look at the average O2 itself (and variance) rather than looking at it only when it is below 90%, which is the definition of hypoxia.

2011 Roctober 19

Cathy Jenkins, Emergency Medicine

  • Designing a study to compare nurses' assessment of vital signs with a robot's assessment of vital signs on healthy volunteers.
  • Can the robot be compared to just one nurse? If not, how would it be analyzed?
  • Change wording of aims from accuracy to agreement.
  • What patient factors influence the robot's agreement with the nurse's measurements?
  • Discussed ways to measure agreement. +

2011 October 5

Khensani Marolen, Anesthesiology
Consultants: Meridith Blevins, Cathy Jenkins, Svetlana Eden, Pingsheng Wu, Steve Ampah, Students

  • Risk assessment tool -- hospital based versus home based. Two reviewers (anethesiologist) review medical history by tool and nurse practitioner, then score. 160 patients with two scores.
    • 1-6 ordinal (1=healthy to 6=extremely sick).
    • Want to assess the differences between two reviewers in one modality
    • suggest (weighted) Kappa -- agreement between reviewers for a given patient - report confidence intervals {http://www2.sas.com/proceedings/sugi22/STATS/PAPER295.PDF}
    • paired Wilcoxon sign rank - test for difference in distribution of scores between reviewers

<-- 
 -->
library(irr)
kappa2(r1[,c("ASA_Score_Experimental","ASA_Score_Control")], weight="equal") 
<-- 
-->

2011 Sep 28

Paul Moore, Susan Hamblin, Pharmacy
Consultants: Frank Harrell, Svetlana Eden, Mario Davidson, JoAnn Alvarez

  • Reducing atrial arrhythmias esp. fibrillation, in trauma patients; role of anti-oxidant therapy (AO)
  • Are applying for VICTR voucher
  • Mainly interested in arrhythmias during trauma ICU stay
  • 7d AO protocol; included if receive >= 72h on the protocol
  • Patients must survive 3d and be in the ICU at least 3d to be in study
  • AKI would have also stopped the protocol
    • How many patients starting the protocol who survived 3d did not finish >=3 3d of protocol
    • Find out how many patients had creatinine < 2.5 at baseline who developed > 2.5 by day 3 that led to interruption of the protocol
  • Retrospective cohort study
  • TRACKS database (trauma)
  • Goal: approximate a randomized clinical trial
    • Time zero (randomization time in RCT) patients start
    • All patients included in analysis
    • Patients are similar between 2 treatment groups or you know the measurements needed to adjust for to make them similar
      • Survey 10 experts not involved in the study, have them list all the reasons for starting such a protocol
      • They must be blinded to the database content
      • Verify that all the variables so named are available from the database or you have variables highly correlated with the needed variables
  • Verify that 100% of persons who qualified for getting the protocol actually got it or that the reasons for not doing it are captured with available variables (or if reasons were random)
    • Can't have comorbidities being the reason for not putting someone in the protocol
  • Need to be accurate in identifying arrhythmia/non-arrhythmia
    • Random sample of 100 patients - read charts to check concordance with ICD9-determined presence of arrythmia
  • Recommended analysis: multiple regression (logistic regression model if outcome is yes/no)
    • Adjust for potential prognostic variables, especially those in the list assembled from expert opinion
  • There is some value in using time until event (censored at discharge from ICU and possible death) because this recognizes that a patient in the ICU a short time has less opportunity for the arrhythmia
  • Do we want to define time zero as the end of day 3?
    • Assume that arrhythmia that happened day 1 - day 3 should be ignored
    • How to account for arrhythmias within first 3d?
  • Verify that arrhythmia surveillance/coding is constant over the years (also the use of APACHE etc.)
  • A good way to justify the sample size is to estimate the number of qualifying patients on each of the two treatment arms, and the overall proportion with arrhythmia, then compute the margin of error (e.g., half-width of the 0.95 confidence interval for the difference in two proportions; fold-change margin of error in estimating a hazard ratio)
  • Suggest applying for regular VICTR voucher, for $4000 of biostatistics assistance (home dept. will need to pay $1000)

2011 September 21

Bret Alvis, Anesthesiology

  • Research question: whether preoperative use of ketorolac prolongs healing of ankle fracture
  • Hypothesis: preoperative use of ketorolac DOES NOT prolong bone healing
  • Data collected:
    • recovery within one month (yes/no)
    • drug use (yes/no) * recommended: to use PS

Chad Wagner, CV ICU

  • Retrospective study. Would like to look at association of time to delirium and 1. pain, 2. different medications.
  • Concerns:
    • pain scale is subjective and depends on many characteristics (age gender) it's important to adjust for these characteristics.

2011 September 14

Michelle Collins, SON

  • Among women with a positive pap smear result, is positivity of biopsy different by hormone usage?
    • Exclusion: Menopausal, Pregnant
    • Inclusion: PAP smear with LOW or HIGH grade result
    • Outcome: Biospy with NEGATIVE, LOW or HIGH grade result
    • Predictor: Hormone Group (Progesterone (IUD or injection), Control)
  • Four possible research questions (need to clarify interest in 'overcall' versus 'false positive'):
    • Is there an association between progesterone condition and LOW or worse grade biopsy for women who screen with LOW or worse grade pap smear?
    • Is there an association between progesterone condition and HIGH grade biopsy for women who screen with LOW or worse grade pap smear?
    • Is there an association between progesterone condition and LOW or worse grade biopsy for women who screen with HIGH grade pap smear?
    • Is there an association between progesterone condition and HIGH grade biopsy for women who screen with HIGH grade pap smear?
  • Null hypothesis: no difference in biopsy result for different hormone groups
    • test this hypothesis with a chi-square test
    • model the odds of positive biopsy for each hormone group using logistic regression; can adjust for potential confounding (e.g. age, progesterone exposure)
      • this requires dichotomizing biopsy result (LOW and HIGH) vs negative
    • consider modeling the proportional odds of positive biopsy for each hormone group using ordinal logistic regression
      • this preserves the ordinal nature of the outcome variable
      • consider an interaction term with PAP smear type and hormone group
        • biopsy = PAP x hormone

2011 August 31

Jim Phillips, Anesthesiology

  • Wants to look at sedative for pain management and safe for sickle cell patients with acute pain crisis in ED
  • Wants to see the blood's effect on 5 biomarkers
  • 30% reduction in pain is considered significant
  • Safety outcomes
  • Wants to design a single arm unblinded pilot study
  • Advised that he might have biostats support through an existing collaboration plan
  • Wants to know sample size
  • 3 to 4 patients may be available for consenting every week. Study needs to be over by next summer.
  • Pain management is main outcome. It will be measured three times for the patient.
  • Could you obtain a control group?
  • Recommended checking pain distribution in other studies.

Wonder Drake, Medicine

  • Want to study immune response in patients with a lung disease compared to healthy controls.
  • Needs to know sample size for grant. Has preliminary data.
  • Main outcomes are measured by flow cytometry and western blot.
  • The important numbers from the previous data are the standard deviation of the outcome.
  • We recommended looking at the PS software and showed some scenarios.
  • If there are more complicated relationships you need to use a model for, you will probably need even more patients than the number calculated by PS for a t-test.

2011 August 24

Jeff Waldman, anesthesiology

  • Research question: whether the use of PVP (peripheral venous pressure), "low PVP technique", instead of CVP (central venous pressure), "low CVP technique", will reduce blood loss and transfusion requirement in hepatic resection.
  • Details: PVP and CVP are correlated and PVP is normally 2 mm Hg higher than CVP
  • Hypothesis: "Low PVP technique" is not inferior to "low CVP technique"
  • Design: double blinded randomised trial. Randomise patients to those whose doctor uses CVP to manage blood loss vs those who uses PVP to manage blood loss.
  • Outcome:
    • blood loss, measured by the volume of blood in a canister measured by 50 cc
    • plus some eye estimate of number of sponges and how blood is in them
  • Concerns:
    • Because PVP and CVP don't agree (2 mm Hg difference) the group with PVP may show less blood loss.
    • Outcome measure (estimate by eye) is a subjective measure
  • Question:
    • how exactly PVP and CVP are related (slope and intercept, mean difference and mean absolute difference)
  • Suggestions
    1. Report CVP or PVP, revealing which one was reported, let medical team make their decisions on an ad-hoc basis
    2. Report CVP or predicted CVP and stratify outcomes by whether real CVP was used
  • Consider VICTR studio

2011 August 17

Patrick Norris, Surgery

  • Wants to apply for biostatistics help through VICTR for writing a grant.
  • Consult on statistical methods and sample size for an R21 submission.
  • Glutamine supplement in trauma patients vs iso-nitogenous placebo.
  • Primary outcome is improved glucose metabolism and stress induce IR
  • Want help with identifying inclusion criteria and sample size
  • May want to select a group of patients for inclusion criteria that the biggest benefit of the intervention is expected
  • One outcome could be the amount of time within range of glucose
  • One suggestion is also to apply for a different grant for a one arm observational study to look at glutamine levels and demonstrate feasibility.
  • We estimate that this work could be accomplished in $2000.

2011 August 9

Thanh Nguyen, Pediatric Anesthesiology

  • Ben Saville was present and we discussed that he or Jonathan Schildcrout may be resources from our department.
  • Looking at emergence delerium after tonsilectomy in children. Wants to look at a combo of two drugs
  • Wants to design a randomized trial to test this, and wants help in sample size.
  • The main outcome is yes/no whether they have emergence delerium, which comes from the PAED scale, which ranges from 0 to 20 and defines emergence delerium if the scale is greater than 10.
  • We strongly recommend letting the original PAED scale, which contains more information and will give greater power.
  • The control group will get a different drug.
  • The literature reports incidence between 10 and 80 percent, but Nguyen estimates that it may be about 40%. The smallest reduction that he considers clinically important is 10%
  • We recommend that he check the literature to find out the mean and variance in the actual PAED, and also how high the scale ranges.
  • We showed him PS software and showed him several scenarios.
  • May want to control for myaps scale, which measured the pre-op anxiety.
  • Planning to set up a redcap database.
  • Discussed stratifying by agitation to make sure the two groups are balanced with respect to agitation (or other important factors).
  • Discussed inter-rater reliability of the PAED scale, and recommended he check the literature for whether there has already been a study on the interrater-reliability of this scale.
  • Discussed pain as an outcome, maybe measured by amount of pain medicine given.

Nick Ettinger, Pediatrics

  • Wants to see if admission times tend to cluster around the times of shift changes.
  • Has data on the 7000 admissions during one year.
  • Start by graphing
  • Need to control for when the patients are arriving in the ED.
  • Could adjust for the number of patients at risk to be admitted at each time, by considering it an offset term.
  • Ben will work with Dr. Ettinger further.

2011 July 27

Raafia Muhammad, Cardiology

  • Discussed how to record survival data: one column for the date, and one for status. The status can be recurred, dead, or neither. If the person is neither, the date column will record their last known date that they were alive. If the person has recurred, the date will contain their date of recurrence.
  • Her main outcome is time to recurrence of atrial fibrillation after ablation procedure. The patients are not considered eligible to recur until after three months after the procedure. After three months after the ablation procedure, if they are still experiencing afib, they are considered as having a recurrence.
  • For the time to event, you will also need the beginning date. This will be the date of the ablation.
  • She also wants to compare those who do and do not have family history of afib among those who have pharmalogical therapy.

2011 July 20

Kevin Sexton, Plastic Surgery

  • Working with Dr. Thayer
  • Wanting to submit a Defense grant that is due 8/25/2011.
  • Wanting Biostats support to (1) review/refine the grant before submission (ie, sample size calculation and statistical analysis plan); (2) determine the details needed for a BCC "contract"; and (2) the actual statistical analysis once the grant is funded.
  • Feel a $2,000 Voucher would be sufficient to review/refine the grant and determine the specifics for the BCC "contract".
  • The BCC "contract" will cover the actual statistical analysis once the grant is funded.

Chelsey Smith, Anesthesiology Summer Intern

  • Health literacy and surgery outcomes
  • Outcomes: length of stay; ER repeats (30 days from hospital discharge)
  • Predictors: measures of literacy
  • Specific aim: determine if there are associations between literacy measures and outcomes
  • Data: all different surgeries; past 10 years
  • Important: many different types of surgeries --- may want to "adjust" for type of surgery or look at only a specific group of surgeries
  • Important: possible multiple surgeries per person in the retrospective data collection timeframe, which could be correlated with each other --- may need to select only one surgery for each patient (depends on the overall proportion of patients who have more than 1 surgery)
  • Important: Elective surgery vs surgeries after being admitted to the hospital
  • Important: will be difficult to tease out coming back because of surgical complications vs coming back because of health literacy issues --- really want to look at those who returned because of health literacy issues
    • In such a large group of data (N ~ 20,000) these numbers may even out
    • Would need to be stated as a limitation of the study

Shilpa Mokshagundam, Anesthesiology Summer Intern

  • Effects of methylphenidate (primary component of ADHD medicine) on time of emergence from anesthesia
  • Population: children on ADHD drugs
  • Four groups of interest: No ADHD, No medication / ADHD, No Medication / ADHD, Medication w/out methylphenidate / ADHD, Medication w/ methylphenidate
    • May want to create more than 1 variable to capture these four groups --- ie, (1) ADHD as No / Yes and (2) Medication as No Medication / Medication w/out methyl / Medication w/ methyl
  • Some children have had multiple surgeries --- may have been on ADHD medication for some surgeries, not on medication for other surgeries
  • Data: 60 kids (w/ ADHD), but ~50 different kind of surgeries; ton of kids without ADHD
  • To adjust for different kind of surgeries: incorporate measures of the different phases of anesthesia --- specifically, measures of induction and maintenance (third phase of "emergence" is the outcome).
    • Also, length of surgery
  • Also: weight, age, current ADHD dosage (or the fact that the child was taking the medication within a specific time frame of surgery)

Karen Kagha, Anesthesiology Summer Intern

  • Time frame: Oct 2004 to March 2011
  • Non-cardiac procedures
  • Time frame covers a period when there were no alerts to clinician to administer beta-blockers; a period when there was a pop-up alert; and a period when there was a hard stop alert (clinician has to either give beta-blocker or give reason why they weren't administered).
    • Pop-ups appear during surgery (ie, interoperatively)
  • Were patient outcomes different between three different periods
    • Thought is that clinicians' compliance should have improved over the three time frames, so patients' outcomes should have improved
  • Beta-blockers given to patients who have specific clinical characteristics
  • Important: need to know (in detail) whether each patient continued to receive the beta-blocker post-operatively if that's when the patient outcomes are measured
  • Thought: exclude emergency surgeries

2011 July 13

Stephen Kappa, Medical Student working in Urology

  • Prospective, randomized, trial comparing stapler vs. ligature during cystectomy surgery for preventing blood loss. Has data from 80 patients. The main outcomes are amount of blood loss and operative time, total device cost, number of additional staples (in ligature group). The number of staples is a cost issue.
  • A clinically meaningful reduction in blood loss is around 300 mL.

  • Another study: prospectively collected radical cystectomy database of ~1000 patients over 10 years. The goal is to look at the overall survival and cancer survival. Want to look at neoadjuvant chemotherapy. They want to look a the frequency of use of chemotherapy in the database over time.
  • Want to know why patients who did not get the neoadjuvant therapy didn't get it. Their question is about how to categorize the reasons why people didn't get the chemo.
  • Question about how to graphically display the usage over time: Error bars suggested to show variation.

2011 June 29

Brannon Mangus, ENT

* Research question: compare two types of surgery: 1) replacing STAPES with a prosthesis or 2) combining STAPES with a prosthesis part using laser. Each surgery was performed on a different population. Type one was performed on 600 patients. Type two was on about 100 patients. We have to compare the outcome and cost.
  • Outcome is the difference between hearing level before and after surgery: less or equal to 5 or greater than 5 decibels.
  • We suggest logistic regression model adjusting for age, gender, race, and the type of surgery.
  • Criticism of this approach: two groups may have different baseline readings (which is not available for the first group). Suggested solution: use current data (second group) only in a regression model with outcome of actual decibels difference between baseline and after-surgery (continuous) adjusted for age, gender, and the baseline reading. If baseline reading isn't significant, we'll feel "better" not having baseline reading in the main model. * Cost analysis. All costs should adjusted to 2010 dollars. Outcome: average procedural cost. Analyse the difference between groups using Wilcoxon Rank Sum test.

2011 June 1

Jesse Ehrenfeld, Damon Michael, Elizabeth Card, Anesthesiology

* First question about how to handle missing data in outcome. Had a non-randomized intervention trial. * Groups assigned sequentially -- those who received standard of care group and then those who received intervention * Outcome was nerve damage after surgery. Outcome was assessed via 30-day follow-up attained from phone calls. * About 20% of intervention subjects could not be reached for follow-up. * Given that it was outcome data missing, suggested describing differences in those lost-to-followup and those with complete information. * Final analysis would need to be on complete cases. * Second question related to vital signs in patients. They have a device that measures the vitals continuously in addition to the values the nurses are taking. They have ~1300 patients and would like to compare the two values.

2011 May 18

Damon Michaels, Elizabeth Lee, Anesthesiology

  • Post operation pain assessment in autistic children who have trouble communicating
  • Want to evaluate a validated tool that assesses pain and discomfort in children who have trouble communicating. This includes parental input. This will be the intervention.
  • The children are all undergoing similar dental rehab surgeries
  • Evaluation using a parent questionnaire
  • Outcomes include pain medicine use, parents survey on satisfaction, length of stay
  • The researchers feel that a 25-30% increase would be clinically significant
  • Recommend controlling for for body mass in the model for amount of pain medicine.
  • Recommended randomization rather than pre-post, but this seems hard logistically.
  • May need to control for medicine received pre and during surgery.
  • Control for autism severity
  • Recommend at least 30 per group, the more the better.
  • Could come back to clinic after around twenty patients have data to recalculate the sample size based on the better standard deviation data.

2011 May 11

Koffi Kla, Stuart Mcgrane, Anesthesiology

  • Hypothesize that nurses in PACU are less experienced in and less likely to have specific training in critical events as compared to nurses in ICU.
  • Will have a lecture in and simulation of a critical event
  • Is doing a pre and post intervention survey in redcap
  • We recommend talking with redcap personnel and making sure that the pre and post surveys can be linked (can match an individual's first response with her second response, although the responses are still anonymous.)
  • Recommend using a visual analog scale (VAS) for overall comfort level in critical situations for both pre and post. That way you can use a regression on the post comfort level, using the pre comfort level as a predictor.
  • If there is an important binary outcome (yes/no), a yes/no answer choice is appropriate. You can use logistic regression for these.
  • Discussed strategies for increasing recruitment.
  • May want to ask each respondent if she is a charge nurse.
  • Interested in comparing the survey result before taking training and after training. The training is focused on educating nursing staff to respond to critically emergent events.

2011 May 4

Tim Geiger, Colorectal Surgery

  • Tim is interested in investigating the association between average intra-op temperature and 1) surgical site infection and 2) length of stay. There are 296 patients and 20% have infection and were primarily discharged within 4 days. Other functional forms of temperature are of interest, but a simplified analysis plan is desired due to presentation deadline (May 14). It is our recommendation that this is doable in 10-20 hours, but Jeffrey needs to be contacted to determine if personnel are available.

2011 April 27

Mike Stoker, Neurosurgery

  • Looking at the association between cerebrospinal fluid leakage and complete reconstruction of suboccipital cranial defects. The main outcome is leakage (Yes/No). There are 100 subjects with one record per subject, and there are 17 events. Reconstruction is a categorical variable Yes/No.
  • Recommendation: to adjust for other variables including previous MVD, age, sex, type of closure (complete, partial, incomplete), and what it was closed with. To avoid overfitting, use propensity score data reduction. To account for surgeon (there are two surgeons), include it as a random effect or to acknowledge in the propensity score.

2011 April 13

Kelli Rumbaugh, Pharmacy

  • Have pre/post data on hemoglobin, hct, for 26 patients on xigris - severe symptom = bleeding.
  • Consider multivariable linear regression if potential confounding exists (e.g. APACHE score).

<-- 
 -->
 Pre_Hgb &lt;- c(10.4,9.7,11.5,13.7,10.6,11.5,12.1,9.3,10.2,9.6,12.5,9.6,6.5,9.8,7.9,8.5,14.5,9.8,7.1,10.2,9.4,13.3,14.8,11.3,8.7,8.7) Post_Hgb &lt;- c(6.8,9.2,10.3,9.9,8.5,9.6,8.7,9.2,8.5,7,10.3,8.6,9.7,7.1,8.8,10.5,9,9.6,11.3,8.3,10,8.4,10.6,9.9,8.1,9.1)

t.test(Pre_Hgb,Post_Hgb, paired=TRUE) diff &lt;- (Pre_Hgb - Post_Hgb) mean(diff)

wilcox.test(Pre_Hgb,Post_Hgb, paired=TRUE,conf.int=TRUE) # results are consistent with t-test

# Null hypthesis: No difference from pre to post xygris # There is sufficient evidence to reject the null hyptohesis of no difference in Hgb from pre to post tx (p=0.008). # The mean difference from pre to post tx is 1.32 dg/ml (95% CI: 0.37, 2.62).

apache &lt;- c(25,27,28,16,27,30,21,30,23,21,32,26,25,30,24,24,25,20,27,26,31,27,32,29,32,27) plot(Pre_Hgb,Post_Hgb) plot(apache,diff)

library(Design) d &lt;- datadist(Pre_Hgb,Post_Hgb,apache) options(datadist="d") # No d -&gt; no summary, plot without giving all details

f &lt;- ols(Post_Hgb ~ rcs(Pre_Hgb,3) + apache, x=TRUE) anova(f) summary(f,Pre_Hgb=c(9,10)) # Pre_Hgb and Post_Hgb have a non-linear association. # The effect of Pre_Hgb on Post_Hgb adjusting for APACHE score no longer achieves statistical significance (p=0.0581). # However, absence of evidence is NOT evidence of absence, so best to quote the effect size (95% CI). # For an individual with Pre_Hgb=9 versus an individual with Pre_Hgb=10, the Post_hgb difference adjusting for APACHE # is -0.37 (-0.74,0). # For an individual with Pre_Hgb=5 versus an individual with Pre_Hgb=6, the Post_hgb difference adjusting for APACHE # is -0.51 (-0.97,-0.05).

par(mfrow=c(2,1)) plot(f) 
<-- 
-->

2011 April 6

Matt Landman, Surgery

  • Is interested in rate of organ donation designation over time. Has county-level data.
  • Poisson regression with a random effects model.
  • Or poisson regression with county and time as a fixed effect and an interaction between county and time.
  • Include county-level covariates, like median income, percent income, etc.

2011 March 9

Joyce Cheung-Flynn, Surgery

  • The project was designed to study the expression level of the HSP27 protein in human saphenous vein remnants obtained from bypass surgery and to determine if HSP27 represents a new vascular biomarker for the metabolic syndrome.
  • Advised that they did not have enough data to run a multivariable regression (n = 11). Recommended scatterplots of the continuous variables by HSP27, and strip plots separately for dichotomous variables, possibly showing another dichotomous variable using color.
  • Excel file: ExcelFile(CheungFlynn_BiostatClinic030911-1.xls)
  • For a separate study, they are applying for VICTR support. One criticism from the VICTR pre-review was that the tests mentioned were incorrect. We reviewed the stats section and improved the wording and made some small tweaks. We also discussed whether a biostatistician will be working on this project. Dr. Cheung-Flynn was planning on doing the analysis herself. I advised to either list herself and give some qualifications for this type of analysis or amend her request to include biostatistical support.

Padmini Komalavilas, Surgery

  • We did some sample size and power analysis in PS software and discussed that these calculations are based on the assumption that the data will come from independent units and that if they get samples from the same patients they will need more statistical methods to analyze it.

Mary Williamson, ENT

  • Is looking at patients with down syndrome who have tonsilectomy they are getting chart reviews. They have 120 records, and so far 15 of them have required a second surgery. They are interested in finding variables that are associated with requiring a second surgery. She is working on a conference presentation at the end of April. We advise that her data would need a lot of work to be analyzable and also that the VICTR statisticians may not be able to accommodate this deadline. We recommended narrowing down the candidate predictors to a handful to avoid overfitting. http://biostat.mc.vanderbilt.edu/wiki/Main/DanielByrne. We estimate that this request can be fulfilled with $2000.
 

2011 March 2

Ted Towse, Radiology

  • Looking at matched case-control study of ALS patients
Line: 9 to 337
 
  • Two time points: time 0 and 6 months later
  • VICTR review
Deleted:
<
<
 

2011 Jan 12

Kathy Edwards: NIH Multi-center vaccine trial -- Dichotomization issue

Changed:
<
<

<-- 
 -->
set.seed(1)
mu <- 100
sigma <- 20
cutoff <- 125
true.prob <- 1 - pnorm(cutoff, mu, sigma) # .106
xlim <- c(0, round(2*true.prob, 2))
nsim <- 10000
par(mfrow=c(4,2))
for(n in c(100,200,1000,5000))
  {
    p1 <- p2 <- double(nsim)
    for(i in 1:nsim)
      {
        y <- rnorm(n, mu, sigma)
        p1[i] <- 1 - pnorm(cutoff, mean(y), sd(y))
        p2[i] <- mean(y > cutoff)
      }
    rmse1 <- sqrt(mean((p1 - true.prob)^2))
    rmse2 <- sqrt(mean((p2 - true.prob)^2))
    eff <- (rmse1/rmse2)^2
    w <- paste('n=', n, '  RMSE=', round(rmse1,4), sep='')
    hist(p1, xlab='MLE of Exceedance Prob', main=w, nclass=50, xlim=xlim)
    abline(v=true.prob, col='red')
    w <- paste('n=', n, '  RMSE=', round(rmse2,4),
               '  Efficiency=', round(eff,2), sep='')
    hist(p2, xlab='Proportion', main=w, nclass=50, xlim=xlim)
    abline(v=true.prob, col='red')
  }
<-- 
-->
>
>

<-- 
 -->
 set.seed(1) mu &lt;- 100 sigma &lt;- 20 cutoff &lt;- 125 true.prob &lt;- 1 - pnorm(cutoff, mu, sigma) # .106 xlim &lt;- c(0, round(2*true.prob, 2)) nsim &lt;- 10000 par(mfrow=c(4,2)) for(n in c(100,200,1000,5000)) { p1 &lt;- p2 &lt;- double(nsim) for(i in 1:nsim) { y &lt;- rnorm(n, mu, sigma) p1[i] &lt;- 1 - pnorm(cutoff, mean(y), sd(y)) p2[i] &lt;- mean(y &gt; cutoff) } rmse1 &lt;- sqrt(mean((p1 - true.prob)^2)) rmse2 &lt;- sqrt(mean((p2 - true.prob)^2)) eff &lt;- (rmse1/rmse2)^2 w &lt;- paste('n=', n, ' RMSE=', round(rmse1,4), sep='') hist(p1, xlab='MLE of Exceedance Prob', main=w, nclass=50, xlim=xlim) abline(v=true.prob, col='red') w &lt;- paste('n=', n, ' RMSE=', round(rmse2,4), ' Efficiency=', round(eff,2), sep='') hist(p2, xlab='Proportion', main=w, nclass=50, xlim=xlim) abline(v=true.prob, col='red') } 
<-- 
-->
  propeff.png

2010 Oct 6

Line: 98 to 394
  Discussed a study that will be comparing children who are cancer survivors. One group receives traditional follow-up while the other group receives a more intensive follow-up. They would like to determine if the children in the more intensive follow-up group has a higher understanding of their diagnosis. He can have 200 patients in each group. We performed a calculation in PS to determine the detectable alternative.

Rachel Idowu, Surgical Resident

Changed:
<
<
Performed a survey for trauma. This looked at their understanding of trauma and where they felt their understanding was. She is going to bring the data back next Wednesday and we will look at some logistic regression models. See GenClinicAnalyses#Rachel_Idowu_Surgery
>
>
Performed a survey for trauma. This looked at their understanding of trauma and where they felt their understanding was. She is going to bring the data back next Wednesday and we will look at some logistic regression models. See GenClinicAnalyses#Rachel_Idowu_Surgery
 

2009 Nov 18

Ken Monahan, Cardiology

Measurement: pulmonary vascular resistance. Gold standard: cardiac catherization. Unit of measurement: WOODS, approximate range: .3 to 20. Normal value is about 1.5. Previous not invasive methods: take ultrasound, then run a regression with gold standard as an outcome and ultrasound as a covariate, then use coefficients to find WOODS given the ultrasound. What is a reasonable sample size. Suggestion by Jeffrey Blume: organize a pilot study of 20-30 patients. Need to know the variability of WOODS and ultrasound, within-subject correlation between two measurements, clinically useful range of the difference. Then estimate the variance of the difference of two measurements and use that estimate to project your sample size based on a confidence interval of this difference. After looking at Bland-Altman plot you may see different variability depending on the value of the measurement (ignore this sentence for sample size calculations. )
Line: 333 to 628
 

META FILEATTACHMENT attr="" comment="For Muyibat" date="1141239792" name="tmp.txt" path="tmp.txt" size="1032" user="ChuanZhou" version="1.1"
META FILEATTACHMENT attachment="propeff.png" attr="" comment="Simulation study showing efficiency of simple proportions with cutoffs" date="1294854310" name="propeff.png" path="propeff.png" size="55696" stream="IO::File=GLOB(0x93b9194)" tmpFilename="/tmp/CLVrDrTin2" user="FrankHarrell" version="1"
Added:
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>
META FILEATTACHMENT attachment="CheungFlynn_BiostatClinic030911-1.xls" attr="h" comment="" date="1299537222" name="CheungFlynn_BiostatClinic030911-1.xls" path="CheungFlynn_BiostatClinic030911-1.xls" size="8704" stream="IO::File=GLOB(0x96ed808)" tmpFilename="/tmp/VX6ZNpOgsM" user="SharonPhillips" version="1"
Revision 36
Changes from r13 to r36
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META TOPICPARENT name="ClinicSurg"

Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

Changed:
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2011 March 2

Ted Towse, Radiology

  • Looking at matched case-control study of ALS patients
  • Two time points: time 0 and 6 months later
  • VICTR review

2011 Jan 12

Kathy Edwards: NIH Multi-center vaccine trial -- Dichotomization issue

<-- 
 -->
set.seed(1)
mu <- 100
sigma <- 20
cutoff <- 125
true.prob <- 1 - pnorm(cutoff, mu, sigma) # .106
xlim <- c(0, round(2*true.prob, 2))
nsim <- 10000
par(mfrow=c(4,2))
for(n in c(100,200,1000,5000))
  {
    p1 <- p2 <- double(nsim)
    for(i in 1:nsim)
      {
        y <- rnorm(n, mu, sigma)
        p1[i] <- 1 - pnorm(cutoff, mean(y), sd(y))
        p2[i] <- mean(y > cutoff)
      }
    rmse1 <- sqrt(mean((p1 - true.prob)^2))
    rmse2 <- sqrt(mean((p2 - true.prob)^2))
    eff <- (rmse1/rmse2)^2
    w <- paste('n=', n, '  RMSE=', round(rmse1,4), sep='')
    hist(p1, xlab='MLE of Exceedance Prob', main=w, nclass=50, xlim=xlim)
    abline(v=true.prob, col='red')
    w <- paste('n=', n, '  RMSE=', round(rmse2,4),
               '  Efficiency=', round(eff,2), sep='')
    hist(p2, xlab='Proportion', main=w, nclass=50, xlim=xlim)
    abline(v=true.prob, col='red')
  }
<-- 
-->
propeff.png

2010 Oct 6

Laura Chang Kit, Urologic Surgery

Is working on two projects. The first concerns a surgery where an artificial sphincter is placed on the urethra for patients with urinary incontinence. The artificial sphincters are measured in circumference, and available in 0.5mm increments. The size used is the smallest size that is larger than the patient's urethra sphincter. The surgery reduces incontinence, but Dr. Chen hypothesizes that the difference in size between the patient's sphincter and the artificial sphincter is related to the amount of post-surgery incontinence. The incontinence is measured as the number of pads the patient uses per day. This is reported by the patient before surgery and at a fixed time point after surgery. We suggested a regression model with the difference in pad use (pre-post) as the outcome, and the difference in sphincter size as the predictor while controlling for the pre-surgery number of pads and some other factors hypothesized to be relevant. We recommend she apply for a $3000 voucher for biostatistics support for an abstract and manuscript and advise that she will need a letter from her department.

Her other project involves a mesh device which is surgically placed around the urethra in women with incontinence. In some patients, the mesh can erode into the urethra or the vagina. She hypothesizes that there are certain risk factors for the erosion, such as higher BMI and smoking, and wants to do a retrospective chart review to identify these risk factors. The problem lies in getting a group of comparable cases and controls. Vanderbilt has many patients who have had this surgery here, but a very small percent of them (about 2 out of 400) have the event. Vanderbilt serves as a referral center for patients in the region who have had surgery elsewhere, but have had erosion and need treatment. She has 36 records of such patients, but there are no controls to go with them. We discussed why the Vanderbilt control patients may not be comparable to these 36 referred with events from various regional hospitals.

2010 Sept 29

Don Arnold, Pediatrics

Rondi Kauffmann, Surgical ICU

Wants to look at the influence of nutrition has on patient outcomes in the surgical icu. Specifically, the time at which nutrition is given is of interest. We discussed controlling for the amount of food and whether the nutrition was given intravenously or through the stomach. She will need to control for how well the patient is doing upon admittance. This is retrospective chart review. We advised to use the time at which nutrition was given rather than grouping early and late nutrition.

2010 Sept 1

Robert Mercie and Fernando Orvalle, Neurosurgery

Retrospective chart review of about 180 records. Evaluating 14 variables' predictive ability for a binary outcome, hemorage during surgery. The main causal outcomes that they have in mind are size of the lesion and the amount of metabolic agent. There are severely limited by their number of events, which is 6. The size of the lesion is correlated with the amount of metabolic agent.

Rachel Idowu, Gen Surgery

Rachel has worked with Meridith to decide on a sampling scheme for sampling hospitals.

2010 Aug 18

Stuart Reynolds Urologic Surgery

Has data from a survey about pelvic symptoms in adult women. Wants to find out which pediatric urologic symptoms are associated with these outcomes. Has about 600 observations. There were four symptoms, each of which are likert scales from never to frequent. One option would be to make one binary outcome based on whether at least one of these was not "never." This outcome could be modeled using logistic regression. We advised that his number of predictors will be limited to the minimum of the number of events and number of non-events. Also, you could fit a separate ordinal logistic regression model for each of the adult symptoms.

We estimated that this project would require about 20 hours of statistical support and recommended applying for funding through victr.

Osgood, Sexton, Hocking, Surgery

Has redcap data on viability of vein grafts. They have about three different outcomes, but one of them only has 15 observations. We recommended only looking at descriptives for that outcome. We estimated that this project would require 30-40 hours, and recommended that the group apply for victr funding.

John Koethe, Medicine

Has about observations on about 800 individuals of cd4 counts over time. They want to look at the association between BMI and change in cd4 counts over time. We recommended using a regression model rather than categorizing and using a Kruskall Wallis test. Got in touch with Cathy, who works with their department.

2010 June 2

Igal Breitman, Surgery

Has repeated measurements from surgery patients. There are two groups. One got a dietary treatment and the other didn't. We recommended a linear mixed model with a random intercept for the ID variable. Igal is interested in finding if there is evidence for an association between the treatment and three continuous outcome variables: glucose, insulin, and and c peptides.

Rondi Kauffmann, Surgery

Rondi has data with multiple observations per subjects. She wants to assess whether a hormone, estradiol, can predict mortality in patients. Sharon suggested a survival model.

2010 Jan 27

Brad Lindell, Med Student

Discussed a study that will be comparing children who are cancer survivors. One group receives traditional follow-up while the other group receives a more intensive follow-up. They would like to determine if the children in the more intensive follow-up group has a higher understanding of their diagnosis. He can have 200 patients in each group. We performed a calculation in PS to determine the detectable alternative.

Rachel Idowu, Surgical Resident

Performed a survey for trauma. This looked at their understanding of trauma and where they felt their understanding was. She is going to bring the data back next Wednesday and we will look at some logistic regression models. See GenClinicAnalyses#Rachel_Idowu_Surgery

2009 Nov 18

Ken Monahan, Cardiology

Measurement: pulmonary vascular resistance. Gold standard: cardiac catherization. Unit of measurement: WOODS, approximate range: .3 to 20. Normal value is about 1.5. Previous not invasive methods: take ultrasound, then run a regression with gold standard as an outcome and ultrasound as a covariate, then use coefficients to find WOODS given the ultrasound. What is a reasonable sample size. Suggestion by Jeffrey Blume: organize a pilot study of 20-30 patients. Need to know the variability of WOODS and ultrasound, within-subject correlation between two measurements, clinically useful range of the difference. Then estimate the variance of the difference of two measurements and use that estimate to project your sample size based on a confidence interval of this difference. After looking at Bland-Altman plot you may see different variability depending on the value of the measurement (ignore this sentence for sample size calculations. )

2009 Nov 11

Dan Barocas, Urology

  • Prostate cancer and predicting upgrading

Fenna Phibbs, Neurology

  • Studying Deep Brain Stimulation in patients with Parkinson's

Marcus Dortch, Pharmacy Trauma

2009 Nov 04

Marcus Dortch, Chris Jones, Surgical Critical Care

Marcus' clinic implemented an antibiotic rotation system, and they want to look at the effect of this system on instance of MDR, drug-resistant infection. We recommended some changes to his data set-up to include the population at each quarter and the number of resistant infections at each quarter. We recommend that he return to clinic with this data set-up, and we will fit a log-linear regression model with negative binomial distribution. One of the important covariates will be whether the quarter was before or after the implementation of the antibiotic rotation system. Another approach would be segmented regression.

2009 Oct 28

Sanjay Athavale, otolaryngology

Had an article with reviewer comments. We recommended some more explanations on some graphs. We also recommended using a logistic regression model instead of several chi-square tests. For a proposal for a new study, we recommended a statistical justification of the sample size.

October 21, 2009

Rondi Kauffman & Rachel Hayes from Surgery

*Cohort
patients in ICU. Patient are checked for blood glucose level and the amount of insulin given to them is adjusted by a protocol. The protocol gives a "multiplier". Based on this "multiplier", the insulin level is changed. Aim: assess trend in the "multiplier" right before the hypoglycemic event. Recommendations: 1. Start from spaghetti plot. Can try time-variant proportional hazard model, linear mixed effect model (since we have repeated measurements).

October 21, 2009

Yukiko Ued from Surgery

*Cohort
Obese patients having a gastric bypass. Measurements: leptin, 8isoP before and after the surgery. Interested in correlation b/w leptin and 8isoP. Recommendation: Spearman's correlation coefficient for correlation. For looking at the difference b/w before and after surgery use Wilcoxon Signed Rank test.

October 14, 2009

Ian Thompson, Urology

*Prospective Study, 17 patients in each arm, main outcome is blood loss, but about 30% get a transfusion. Need to adjust for amount of fluids received and baseline hematocrit. Goal is to compare two devices used in surgery in terms of blood loss. We have informative censoring. Use a t-test or regression comparing amount amount of blood lost during surgery. Use the amount collected "in the bucket" as the outcome. Overall estimated difference in blood loss.

September 23, 2009

CJ Stimson, MD student

  • Had survival data for patients who had radical cystectomy.
  • Wanted to test difference in hazard functions for male and female patients, since a difference was found in another study.

Rachel Hayes, Informatics

  • Wants to find the best way to determine a cut point incorporating positive predictive value.

September 16, 2009

Jonathan Forbes, Neurosurgery, PGNY4

  • plotted data from the study on MRI characteristics of cerebellar neoplasms * R code for plots #Clear existing data and graphics rm(list=ls()) graphics.off() #Load Hmisc library library(Hmisc) #Read Data data=read.csv('DATA_WHPEDS_FOSSA_NEOPLASM_FORBESJ1_2009-09-16-12-24-20.CSV') #Setting Labels
label(data$mrn)="Medical Record Number" label(data$criterion_1)="Criterion 1: Diffusion Restriction" label(data$criterion_2)="Criterion 2: T2 Hyperintesity" label(data$criterion_3)="Criterion 3: Laterality" label(data$criterion_4)="Criterion 4: Tumor Exit" label(data$dwi_cgm)="Relative DWI Value of Cerebellar Grey Matter" label(data$dwi_cnp)="Relative DWI Value of Cerebellar Neoplasm" label(data$t2_hi_tumor)="Relative T2 Hyperintensity of Tumor" label(data$final_path)="Final Pathology" #Setting Units

#Setting Factors(will create new variable for factors) data$criterion_1.factor = factor(data$criterion_1,levels=c("2","0","-1")) data$criterion_2.factor = factor(data$criterion_2,levels=c("-1","0")) data$criterion_3.factor = factor(data$criterion_3,levels=c("-1","0")) data$criterion_4.factor = factor(data$criterion_4,levels=c("0","1")) data$final_path.factor = factor(data$final_path,levels=c("0","1","2","3","4","5","6"))

levels(data$criterion_1.factor)=c("DWI Hyperintense","DWI Isointense","DWI Hypointense") levels(data$criterion_2.factor)=c("T2 Isointense","T2 Hypointense") levels(data$criterion_3.factor)=c("Hemispheric","Midline/Indeterminate") levels(data$criterion_4.factor)=c("No tumor exit from Luschka/Magendie","Tumor exit from Luschka/Magendie") levels(data$final_path.factor)=c("Ependymoma","JPA","Medulloblastoma","Other","Other","Other","Other") names(data) = gsub("_", "", names(data))

#pdf("dwi.pdf", height=8, width=8) color="gray55" jpeg("dwi.jpeg", width=8, height=8, units="in", quality=100, res=600) boxplot(data$dwicnp ~ data$finalpath.factor, outline=FALSE, border="black", ylab="Relative Diffusion-Weighted Intensity (DWI)") stripchart(data$dwicnp ~ data$finalpath.factor, add=TRUE, method="jitter", jitter=.1, vertical=TRUE, pch=19, cex=.8) abline(h=c(1, 1.35), lty=c(2, 3), col=color) legend("topleft", legend=c("Relative DWI of Cerebellar White Matter", "Relative DWI of Cerebellar Grey Matter"), lty=c(2,3), col=color) dev.off()

  • Second study
    • study on trigeminal neuralgia looking at outcomes when the by the replacement of the bone from the craniotomy
    • craniotomy for pain relief by moving the blood vessel off the trigeminal nerve

September 9, 2009

Sylvie Akohoue, gastroenterology

  • We look into risk factors colon cancer in morbidly obese patients.
  • We collect hormones, bio-markers, weight.
  • Design: 4 groups, with 8-10 subjects in each group, 1 - control, 2 - diet, 3 - exercise, 4 - diet + exercise. They are followed for 12 weeks. The data collected at the baseline and at the end of the study. For groups 2 and 4, the weight data is collected each week.
Analysis
linear regression; outcome- 12-week reading, covariates - baseline reading + treatment group (four levels: see the design). (Find out how SPSS defines reference groups). Analysis of the effect of physical exercise to weight change (for groups 2, 4): look at the average weight for every week and see what group decreases more.

September 2, 2009

Bill Heerman, med peds resident

  • Working on understanding analysis in a paper about a plavix study. The study looked at duration of plavix therapy. We went over some of the concepts: splines, competing risks.

Marc Bennett, Alejandro Rivas, ENT

  • Working on setting up database.
  • Looking at ear surgery patients. Want to track information such as complications. One goal is to be able to compare outcomes with other institutions.
  • TIme variables? They need to follow longitudinally over time.
  • Current software? Underlying structure?
  • Pros and cons of using RedCap. Contact Janey Wang janey.wang@vanderbilt.edu about getting current data imported.
    • Pros: Free. Easy to build. Easily exported to different statistical software.
    • Cons: Not good at handling longitudinal data?
  • Microsoft Access would be an alternative.
  • Ways to directly access the data from star panel?

August 26, 2009

Steve Deppen, Thoracic Surgery

  • BMI vs. resource use in lung cancer surgical resection
  • Research by others has viciously dichotomized BMI
    • relationship may be non-monotonic; need a smooth nonlinear relationship
  • Relating complications to obesity is also of interest but the multiplicity of complications is a problem
    • May be helpful to score complications against some other outcome as has been done by G Marshall, F Grover, W Henderson, K Hammermeister in the cardiothoracic surgery literature
  • Dataset has height and weight so can do a statistical test of the adequacy of the BMI formula in predicting risk, e.g., try adding log(weight) to a model that has log(BMI) in it to check if the coefficients of log(weight) and log(height) have a ratio of 1 : (-2)

July 15, 2009

Raphael See and Lisa Mendes, Division of Cardiovascular Medicine

  • End stage renal disease; nuclear perfusion imaging: dobutamine stress thought to be less effective in ESRD
  • Goal is detecting/quantifying coronary artery disease (CAD)
  • Head to head assessment of CAD detection ability with 2 diagnostic modalities
  • Prevalence of CAD around 0.2
  • Can take as the goal to estimate the difference of diagnostic accuracy of the two methods or alternatively to estimate the probability that method 1 is more accurate than method 2
  • Nuclear perfusion imaging provides segmental perfusion assesssment (17 segments)
  • Stress echo uses wall motion abnormalities in segments (but different segments)
  • Ischemia, infarct, LV function can all be quantified
  • Referral bias: negative tests less likely to lead to cardiac cath
  • Would CT angiography be an alternative? Calcification is a problem.
  • A hybrid design would be to always send to cath every patient who is positive on at least one of the two tests
  • If use a CAD severity index (ordinal scale with 10 or so levels) can correlate each noninvasive test index with this severity scale and assess the difference in two rank correlations (correlated correlations; could use the bootstrap to get the final confidence interval)
 

May 27, 2009

Changed:
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Kathie Hartmann, Biomedical Informatics

>
>

Kathy Hartmann, Epidemiology/Ob/Gyn

 
  • Question concerning grant.

Apr 29, 2009

Tom Compton, Biomedical Informatics

Line: 81 to 277
 

Wes Ely and Delirium Assessment study group

*Goal
Development of a score for delirium assessment (severity vs accuracy).
*CAM-ICU
based on 4 features. 1,2,3 or 1,2,4 ==> delirium. Derive a point system, assign points to features, validate the scoring system. Clinicians can do at bedside. Feature 1: 0/1, feature 2: 0-10, feature 3: 0-5, feature 4: -5 to 4.
Changed:
<
<
*Questions
Is it for diagnosis or risk stratification? Which patient population? What clinical outcomes - mortality, long time cognitive function, differential response to treatment? Do features have equal weights? *Current databases: VALID Database: 131 patients, daily assessment of ICU for each of the features, followup to max 14 days, about 5.5 days per patient, all ICU patients, Delirium can be on and off over time. MINd/MEND: 210 patients, with short term cognitive outcome, up to 21 days Delirium/Mortality(JAMA): n=275, with six month survival data *Outcomes:death, ICU LOS, Hospital LOS, long term CI
>
>
*Questions
Is it for diagnosis or risk stratification? Which patient population? What clinical outcomes - mortality, long time cognitive function, differential response to treatment? Do features have equal weights? *Current databases: VALID Database: 131 patients, daily assessment of ICU for each of the features, followup to max 14 days, about 5.5 days per patient, all ICU patients, Delirium can be on and off over time. MINd/MEND: 210 patients, with short term cognitive outcome, up to 21 days Delirium/Mortality(JAMA): n=275, with six month survival data *Outcomes:death, ICU LOS, Hospital LOS, long term CI
 
*A
For risk stratification, the scoring system should be outcome specific. Look at the raw profiles of each feature. Look at how big changes in profiles relate to the outcome. Modelling individual features in the regression models, but this is not the same as directly modelling delirium, still maybe useful. Come up a theoretical framework first instead of emprically deriving it. Go back and rescore each of the 4 features, then you have to go through the estimation and validation for each one. Item-response, split sample, psychometics method. When a score is derived, add other features deparately into the model, this is to test the adequacy of other components or information loss.

28 Jan 2009

Line: 137 to 332
 
  • Recommend propensity scores, can be used in both matching and in analysis

META FILEATTACHMENT attr="" comment="For Muyibat" date="1141239792" name="tmp.txt" path="tmp.txt" size="1032" user="ChuanZhou" version="1.1"
Added:
>
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META FILEATTACHMENT attachment="propeff.png" attr="" comment="Simulation study showing efficiency of simple proportions with cutoffs" date="1294854310" name="propeff.png" path="propeff.png" size="55696" stream="IO::File=GLOB(0x93b9194)" tmpFilename="/tmp/CLVrDrTin2" user="FrankHarrell" version="1"
Revision 13
Changes from r1 to r13
Line: 1 to 1
 
META TOPICPARENT name="ClinicSurg"

Data and Analysis for Surgery, Anesthesiology, Emergency and Critical Care Medicine Clinic

Added:
>
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May 27, 2009

Kathie Hartmann, Biomedical Informatics

  • Question concerning grant.

Apr 29, 2009

Tom Compton, Biomedical Informatics

  • Studying accuracy of nurses recording of glucose measurements compared to what's recorded on the glucometer.
    • Are the nurses recording accurately?
  • Now studying "multiple levels of inaccuracy"
  • 25 values per day per patient, about 1900 patients in one ICU, approx 90,000 values
  • Is there a relationship between errors and blood glucose variability?
  • May want to consider looking at length of stay, where/when in the hospital are these errors occurring, range of errors.
  • Concern: What if, for example, it's a trauma patient with high variability initially and all of the errors occurred after they stabaliized?
  • Recommended requesting a VICTR voucher.
 
Added:
>
>

Apr 22, 2009

John Wood and Brian Burkey, Otolaryngology

  • 110 patients, retrospective study, all possible selected from the late 90's on.
  • Only 2 cases with hand function deficits
  • Looking to create a predictive model of morbidities
  • 3 tests: Could create at 2x2 tables, look at Kappa statistics
  • Created 3 2x2 tables, doppler vs allens; doppler vs surgical allens; allens vs surgical allens.
  • Is somebody with diabetes/coronary artery disease/etc more likely to lose their flap?
  • Recommend getting a voucher from CTSA for more statistical help.

Apr 8, 2009

CathyJenkins, Pediatric Emergency Medicine

  • Measurements of severity of asthma attacks
  • about 90 patients

Patrick Norris Phd, Trauma

  • New way of measuring bone density, doesn't require a DEXA scan, just a CT
  • Some populations commonly get these scans: older women, people on chronic steroids, etc.
  • Have DNA measurements of a group of these people
  • Defense department is interested in this study; finding young men getting stress fractures; not all men are getting these. Is there a genetic explanation?
  • Continuous outcome.
  • Sharon recommends looking at Ordinary Least Squares Model and/or Proportional Odds.
  • Primary analysis: use OLS, but for "clinical interpretation" use PO.

Feb 22, 2006

Patrick, Trauma

  • Relationship between intracranial pressure(ICP) and reduced heart rate(HR) variability
  • Look for non-invasive monitoring of brain trauma patients
  • less variability with ICP - due to low dynamic range?
  • N=146 dinstinct patients, with 4~5 days of data (every 5 minutes), high mortality patient population
  • avoid uncoupling?
  • regress current on previous time point, prediction as a function of time lag, different behavior between live or die. Is it due to difference in HRV or sampling? Informative missing?
  • determine predictors of completeness
  • take out any indices related to completeness
  • wider sampling intervals, use 1 hour interval instead of 5 minutes
  • two completeness indices: % complete and longest gap, use multivariable logistic model to find predictors of completeness
  • therapeutic scoring system, how intensively the patients were treated
  • repeat completeness analysis with and without zeros
  • Identify patients with zeros and ECG measures, and see what zeros are doing
  • Graphs: show some individual patient data

Mar 1, 2006

Mark Kelley and Brian Gray, surgical science

  • Q: relationship between time of tissue out of body and immunohemo characterization of tissue cells, zenograph of tumor
  • Q: quantify chemical stains - different tumors have different stain patterns
  • How to test for trend
  • grow tumor in mice, take biopsy, divided into samples, let them sit outside for a while, do hemochemical stain
  • validatione: a pathologist resident redo a subsample, blinded
  • How to organize the data
  • three outcomes: score, intensity and pencent of positive cells on each sample at each time; 7 tumors, 4 time points, 3 replicates, 8 stains
  • sources of variations: tumor type, samples, time, stains
  • nested block design:
  • estimate trend within the same tumor type and stain method
  • deal with the 3 outcomes separately
  • A: repeated ANOVA, test for time effect

Muyibat Adelani, Vascular Surgery

2 May 2007

Greg Polkowski, Orthopedic Surgery

Sample size for paired t-test using estimated standard deviation of within-cadaver differences from existing data (n=5). Power of 0.9 was used, sample was estimated to be 15 pairs.
<-- 
 -->
 # peak load x &lt;- c(100, -200, 300, 0, 100) # differences resected vs. control sd(x) # 182 # energy to fracture x &lt;- c(3.7, 0.1, 2.7, -0.2, 1.6) sd(x) # 1.67 delta &lt;- .15*mean(c(20.1, 3.2, 4.5, 20, 22.4)) # 2.1 used 1.5 
<-- 
-->

23 May 2007

Wes Ely and Delirium Assessment study group

*Goal
Development of a score for delirium assessment (severity vs accuracy).
*CAM-ICU
based on 4 features. 1,2,3 or 1,2,4 ==> delirium. Derive a point system, assign points to features, validate the scoring system. Clinicians can do at bedside. Feature 1: 0/1, feature 2: 0-10, feature 3: 0-5, feature 4: -5 to 4.
*Questions
Is it for diagnosis or risk stratification? Which patient population? What clinical outcomes - mortality, long time cognitive function, differential response to treatment? Do features have equal weights? *Current databases: VALID Database: 131 patients, daily assessment of ICU for each of the features, followup to max 14 days, about 5.5 days per patient, all ICU patients, Delirium can be on and off over time. MINd/MEND: 210 patients, with short term cognitive outcome, up to 21 days Delirium/Mortality(JAMA): n=275, with six month survival data *Outcomes:death, ICU LOS, Hospital LOS, long term CI
*A
For risk stratification, the scoring system should be outcome specific. Look at the raw profiles of each feature. Look at how big changes in profiles relate to the outcome. Modelling individual features in the regression models, but this is not the same as directly modelling delirium, still maybe useful. Come up a theoretical framework first instead of emprically deriving it. Go back and rescore each of the 4 features, then you have to go through the estimation and validation for each one. Item-response, split sample, psychometics method. When a score is derived, add other features deparately into the model, this is to test the adequacy of other components or information loss.

28 Jan 2009

Parker Gregg, Medical Student

  • Doing a pilot study in Guatemala to study two different methods of treatment compared to both each other and a SOC
  • "Treatments" are different methods of educating patients with STDs to educate their partners
  • Initially considered doing a crossover study with three clinics - two change treatments and a third just does SOC
  • Goal of study is to increase the number of people who need to be treated for STDs by telling patients to tell their partners. SOC of does not include this extra step.
  • Recommended to ignore clinic effect since it is such a short study over the summer - do one treatment in each clinic. There would need to be a good washout period in between treatments, and there is not enough time to do so. Also, all three treatments would be needed at all three clinics to consider this a true crossover study.
  • Need to answer the question "What to measure?" Just a global number showing a patient increase or track individual patients and who they recommend to come back.
  • Can you get information on patient volumes from the summer before to compare?
  • Recommended giving color coded cards (color based on the clinic) to count referrals.
  • Send survey to MarioDavidson once completed.

18Feb2009

Christina Edwards, Surgery

  • Studying how often people go to the ICU after a Whipple procedure
  • Consider a logistic model with restricted cubic splines for OR Time, estimated blood loss.
  • Include other variables such as age, may want to use splines for that variable as well.
  • Examine the ROC curve for the logistic model as a measure of classification ability.
  • Examine pseudo-RSquare and other statistics as well.
  • Fit and examine various models, CAD seemed to be an important predictor.

04Mar2009

Doug Atkinson, Pediatric Critical Care

  • 20 patients, 4 measurements each about 4 hours apart, 1 missing point
  • comparing devices used to get measurements of blood oxygen saturation
  • What statistical tools to use?
  • Is a sample size of 20 enough? Go ahead and do the analysis, see if there is anything conclusive. If the confidence intervals are too wide, may need to add more patients.
  • Make a scatter plot of all 80 points across time.
  • Several scatter plot suggestions: Measurement 1 vs Truth, Measurement 2 vs Truth, Measurement 1 vs Measurement 2
  • Kappa statistic - for first aim
  • Can fit a regression model but need to take into account that data within each patient is correlated.
  • See spreadsheet tips at the bottom of this page.
  • For statistical help, may apply for a voucher through the CTSA.
  • Recommend getting STATA to do statistics
  • Check with department head to see if you have a collaboration plan.

PingshengWu, Diabetes Center

  • Don't worry about Bonferroni adjustments when doing power and sample size analysis.
  • Do false discovery rate analysis at the end.
  • Do one F-test for the ANOVA

11Mar2009

Dr. Meghan Lemke, Pulmonary and Critical Care

  • Has a poster presentation this weekend, question about graphs.
  • If you can get the raw data, email JoAnnAlvarez and she can make some graphs
  • For p-values, use Wilcoxon Signed-Rank Test

Dr. Thomas Pluim, Pediatric Critical Care

  • Case-Control study
  • Recommend propensity scores, can be used in both matching and in analysis

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