Recommendations, Analyses, and Data for Health Services Research, Diagnosis, and Prognosis Clinic

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Current Notes


Raymond Zhou, Vanderbilt Eye Institute

Previous clinic sessions 8/17/20, 7/23/20, 4/16/20

We aim to correlate known single nucleotide polymorphisms (SNPs) in CXCL8:CXCR1/2 with Diabetic Retinopathy (DR) susceptibility and progression in cohorts of patients with Diabetes Mellitus (DM). We have developed 5 cohorts of patients with varying degrees of DR (No DR, Non-proliferative DR, Proliferative DR, Macular Edema, Vision-threatening DR) using Vanderbilt’s synthetic derivative (SD). From previous Biostats Clinic sessions, we developed a plan to test our hypothesis by comparing the prevalence of SNPs in CXCL8:CXCR1/2 between our groups. We have yet to obtain genotyping data, and are interested in analyzing baseline clinical characteristics between our groups of interest. This will help us validate the validity of our cohorts, by allowing us to confirm that the cohorts exhibit expected clinical characteristics. My goal is to show that, consistent with what we might expect these diagnoses/treatments are more prevalent/frequent in patients with more severe DR. Through excel, I have applied Pearson’s test to compare prevalence of diagnoses (e.g. nephropathy) seen with Diabetes, as well as frequency of treatments (e.g. vitrectomy) provided for Diabetic Retinopathy (DR) between my groups of interest. I would like to discuss the appropriateness and interpretation of my analyses, and identify different analyses that I may need to work on as well. I understand the Chi-square analysis is a test for any difference and cannot comment on whether prevalence is higher in one group than the other. Thus I used the phrase “significantly different” and added that prevalence was highest in PDR. This same wording (“significantly different, with unreported ethnicity highest in children < 3 years old”) was used in my ARF manuscript. Is this valid? For the comparisons between No DR -> NPDR -> PDR, we would expect a stepwise increase in prevalence/frequency—lowest in no DR, highest in PDR, with NPDR in the middle. My initial approach was to use a 3x2 contingency table Pearson’s test to show significant difference, then comment that prevalence was highest in PDR. Would it be better to execute separate Pearson’s tests between No DR and NPDR, as well as NPDR and PDR, to confirm differences between the individual groups? Is there another test that is more appropriate in this case?

Clinic Notes:
  • Looking to correlate genetics with diabetic eye disease. 5 cohorts with varying severity of diabetic eye disease.
  • Completed comparisons via chi-square test, is this correct? Just looking at baseline without controlling for anything (conducting analyses themselves)
    • Are there any confounding variables (ex. age)? Put more focus on difference in proportions (absolute effect) instead of p-value (can also do odds ratios for relative effect)
  • Comparing 2 factors (at least 1 with more than 2 levels [2x3 table]) - use chi-square, if chi-square is large you can talk about apparent differences (proportions) and not worry about statistical tests once you do the "overall" chi-square.
  • Confidence intervals (CI) are a good way to present results. The limitations are self contained - if you have sample size of one comparison that is not large, that could be a reason that chi-square gives you small value, in this case CI would be wide and let you know that you cannot conclude anything - it is uncertain. Descriptive studies it is sometimes better to present CI. Present them any time attempted to say the p-value is big so we think there is no difference. "Absence of evidence is not evidence of absence". Big p-value means you need more data, not that there is no difference (ie. should say, we do not have enough evidence to conclude that there is no difference or we were unable to find evidence that there is a difference).


Laurie Samuels, Biostatistics

I would love help thinking through a hybrid implementation science/clinical trial design.

Clinic Notes:
  • Grant proposal due in June. Cluster randomized, with families in the cluster. Intervention is a curriculum, measuring effect of telling PMs at community centers they can modify a tested curriculum. Outcome is BMI of kids of all ages. Collaborator wants to use BMI percentile (per CDC), which is problematic. You should percentile something only if 1) you don't understand measurement or 2) if there is competition. Can make value judgments based on height and weight in regards to BMI.
  • Should look at out come you want, intended audience, potential journal (might be more of a policy type study, can advise on how to design). Suggest having design studio.
  • Helpful link:


Dakota Vaughan (Sean Donahue), Ophthalmology

We’d like to address the feasibility of generating a predictive model for amblyopia or its risk factors, given data generated during preschool vision screening. Mentor confirmed.

Clinic Notes:
  • Want guidance on practicality of the project, multivariable logistic model.
  • Sample size is >2,000. ~60 have the outcome of interest. Need roughly 15 events of the outcome per predictor in the model. Might need 2-3 terms for a continuous variable if not linear relationship between it and the outcome (ex. age - if outcome increases exponentially at a certain age). Age should be controlled for, in months or even days. Resource:
  • Outcome: Binary (need for follow-up/not due to presence of amblyopia on eye exam). Suggest using graded scale if possible, then convert clinical model (binary).
  • Validation of model: resampling method (ex. bootstrap) or cross validation, rms allows you to do this (ex. "validate()"). Do not exclude variable based on how it correlates to outcome.


Santiago Angaramo, Ophthalmology and Visual Sciences

Had a prior email communication with Dr. Harrell. I am currently looking at our collected data to see if HTN has any effect on the development of Diabetic Macular Edema (DME). To do this I divided our original “cohorts” into two groups +DME and -DME. Below are some values of our current data that I have worked. There is concern that type 2 DM vs Type 1 maybe causing a confounding effect in the relation between HTN and DME. However my mentor and I were wondering if there was a better way to analyze the potential compounding effect of HTN and DM2? Mentor confirmed.

Clinic Notes:
  • Cohort defined by ICD10, confirmed by chart. This is a sub-study or a larger study.
  • Patients with DM type I and 2, with hypertension. Confounders include type of DM and hypertension (exposure), age, race. Outcome of interest is diabetic macular edema. PHEWAS project.
  • Propensity score or regression?
    • Either could work, would depend on the number of confounders. Rule of thumb for number of confounders in regression - have absolute minimum 5 events per confounder/category of confounder, typically use 15-20 events. Propensity score is used as a data reduction method - typically used when you have a low sample size or a large number of confounders.
    • Do not categorize continuous variables (A1C, age). Don't let size influence confounders--include all that are medically relevant.
  • How many patients needed for prelim power analysis?
    • Go with max number you can get and then find the power you have if you include them all. Since you are not allowed to choose your sample size, you are finding how adequate the sample size is for your questions. Max power is having equal non-event to events, but do not manipulate that, go for the max you can get.
  • First steps:
    • Establish and define confounders (discuss with 5 clinical experts)
    • Document all decisions with detail
    • Understand capacity of EMR and what you can get


Benjamin Park (Kyle Higdon), Plastic Surgery

Previous clinic session: 2020-08-31
Follow up on statistical planning for the project to review the impact of the selection of smooth versus textured tissue expanders in prosthetic breast reconstruction in post-operative complications and outcomes at the Vanderbilt University Medical Center. Mentor confirmed. VICTR voucher request.

Clinic Notes:
  • Retrospective study, ~1600 possible participants. Would like VICTR voucher. Primary question is safety differences. Frank: we want to be sure to capture the entire population, don't filter out cases on data pull side. Multiple complications are possible. Follow up typically once a year, after initial healing period. Could do a time to event analysis with KM curve by group. Could also estimate risk over time by group. Kent: could do early complications, perhaps in 6 months post surgery?
  • Recommendations:
    • Descriptive statistics (Kaplan-Meier estimator)
      • Need to take in to consideration time to event - Cumulative incidence curve (1 - kaplan-meier survival curves)
    • Primary Analysis: Cox Proportional Hazard Model
    • Sample size: Safety outcomes look at absolute risk, not relative risk. Typically, size based on number of complications to detect differences (rough rule - for cox model need at least 15 events per variable you want to study as covariate). Say for estimating absolute incidence, 1600 will give good precision of incidence estimates, but estimating relative rate of complications between groups, would need (15 events x # of variables).
    • VICTR application: include inclusion criteria (how you get analysis population), work out definition of chart review outcome, have plan to ensure that you are doing chart reviews correctly.

NO SHOW: Jeremy Joseph (Kyle Higdon), Plastic Surgery

Venous thromboembolism (VTE) is a well-known complication after surgery and amongst plastic surgery patients, Abdominal lipectomy patients are one of the highest risk populations. This project will focus on identifying a method of improving and promoting early ambulation after abdominoplasty. Using actigraphy devices such as the Fitbit Inspire HR ® (San Francisco, California USA), monitoring activity levels has become more accessible. In addition, these devices can also encourage and remind wearers to get active, especially if the wearer is below a previously defined goal or their baseline level. We hypothesize that patients undergoing Abdominal lipectomy are more likely to return to their baseline activity status sooner when they are reminded to get active by an actigraphy device. Our goal is to determine if actigraphy devices are useful in encouraging post-operative ambulation and in helping patients return to their baseline sooner than if they did not receive these reminders. If there is a significant difference in these two groups, it may suggest that actigraphy devices be used in high risk surgery to promote ambulation with the ultimate goal of preventing VTE. Most urgently for this meeting, we need to get a quote for the expected costs for the biostatistical support in order to submit for our grant which we would like to turn in the same day or the following day after this meeting. Mentor confirmed.


Audrey Bowden, Biomedical Engineering

We are interested in the differentiation of bladder carcinoma in situ from background inflammation. We plan to use a light-based technology on ex vivo tissues to establish a set of evaluation criteria. We will attend the meeting from 12:30pm due to a time conflict. VICTR Voucher request.

Clinic Notes:
  • Current VICTR voucher for this. This is an early stage study. Prelim data for a larger study.
  • Q: How big of a sample size do we need?
    • Minimum of 96 patients to estimate a probability (sensitivity) no worse than +/- 0.1 of the truth. Every time you want to be twice as precise it take 4x the sample size (ex. +/- 0.5 = 96x4). Notes showing formula for 96: Page 5-45.
    • Consider indeterminates, rather than just clear yes/no. Indeterminates are often the "gray area" where we need more help identifying the correct answer. Would suggest not limiting to only sensitivity or specificity, but do both. ~200 samples as suggested may be prohibitively expensive. Could do less, but need to be clear about reason why it is lower. Need to set a minimum sample size for the hardest category to sample as this would be your limiting case - other categories can be more.

Eva Mistry, Neurology/Stroke

I want to understand how I can calculate confidence intervals around the predicted probability of an outcome based on my regression model.

Clinic Notes:
  • Q: How to compute 95% CI for probability estimate - Stata gives 95% confidence interval around coefficient.
    • This function should available in Stata. Ignore CI on coefficients, need CI on predicted value. Email Tom if questions persists as he is resident Stata user.


Benjamin Park (Kyle Higdon), Plastic Surgery

This would be a retrospective chart review study at VUMC that would be comparing postoperative clinical outcomes (infection, contracture, necrosis, etc) between two groups with different devices (smooth versus textured tissue expanders) used in prosthetic breast reconstruction. We are currently applying for a VICTR grant that requires biostatistical support. Mentor confirmed. VICTR voucher request.

Clinic Notes:
  • Comparing safety and efficacy of textured vs smooth tissue expander. Rare cancers linked to textered expander, recall issued in 2019.
  • Retrospective chart review, EMR with CPT code, will use RedCap
  • Clearly specify the definitions for the outcomes and how those outcomes will be defined using ICD/CPT codes.
  • When extracting data - be sure to include calendar time in data (dates). Consider factors that go into patient and provider decision making. May need to get physician level data.
  • Timeline - finish by December
  • Analysis Notes:
    • Emphasize descriptive methods and difference in groups, 95% CI should be used for differences in proportions. Univariate analyses are not really a good way to inform multivariate analyses.
    • Could try to predict which device they received by using profile of the outcomes (secondary to main). Logistic regression model. This says there is a difference in the complication rates among the two. Looks at joint action of all the complications together.
  • VICTR Award for biostatistics support (90 hours): application website ( and research proposal template (


Julie Hudson, Medical Education & Administration

The study involves identifying a matched cohort to a group of participants in a summer research internship program and tracking both groups for at least 3-5 years. The endpoint is entry into the STEM workforce or pursuing advanced degrees in STEM.

Clinic Notes:
  • Summer Research Program (completed 11 years, 15-20 students per year) - tracked every student and their career progression in STEM careers
  • Been told need to identify matched cohort and track that cohort as well
  • Have IRB submission but want to ask questions prior to submission - possible VICTR voucher in future but need "planning" right now.
    • Are we on track for length of study and numbers we need?
    • Are we missing data points?
    • Outcomes:
      • Primary outcome - do they persist in STEM career?
      • HS Students - how many go out of state to 4-year college (largely from rural locations)?
      • Other: Where they go to college, what they do when they complete college (workforce, advance STEM degree, non-STEM workforce, etc.).
  • Need to define/finalize outcomes
  • Could base sample size on specific outcomes; however, since you are limited in funding, sample size (dichotomous outcomes) may be too high to achieve.


Raymond Zhou, Vanderbilt Eye Institute

Previous clinic sessions 7/23/20, 4/16/20

We aim to correlate known single nucleotide polymorphisms (SNPs) in CXCL8:CXCR1/2 with Diabetic Retinopathy (DR) susceptibility and progression in cohorts of patients with Diabetes Mellitus (DM). We have developed 5 cohorts of patients with varying degrees of DR (see figure) using Vanderbilt’s synthetic derivative (SD). From previous Biostats Clinic sessions, we developed a plan to test our hypothesis by comparing the prevalence of SNPs in CXCL8:CXCR1/2 across our ordinal cohorts using a Proportional Odds Regression Model. During our 4/16 clinic session, Dr. Harrell had suggestions on how to divide our cohorts into the finest gradation, and we wanted to follow-up on this, especially if it applies to how we should collect/organize data (see forwarded document with questions in email chain). During our 7/23 clinic session, we also discussed difficulty with strongly ordering 2 (Non-proliferative DR+Macular Edema vs. Proliferative DR-Macular Edema) of our 5 cohorts, and came up with two solutions: separate +/-macular edema and +/-proliferative/non-proliferative disease into two separate variables/categories OR combine the two cohorts into the same ordinal level. We wanted to discuss potential strengths and weaknesses of both approaches, especially as they might affect sample size/power. Lastly, during our most recent team meeting, we reviewed some comparisons between specific groups that we are most interested in. These include 1) Control patients (no DR at all) vs. Proliferative DR+Macular Edema (most extreme form of disease), 2) Control patients vs. ALL Macular Edema, 3) Control vs. All PDR, 4) All Macular Edema vs. All no Macular Edema, 5) Control vs. All NPDR vs. All PDR We wonder whether having these individual comparisons in mind might guide how we structure our ordinal cohorts and Regression model.

Email thread:

  • 1. “Do NOT treat them as distinct cohorts but as graduation (Outcome variable is 1-4, not 0 vs 1 vs 2, vs, 3, vs 4). If strongly ordered, Y=1-4 and use ordinate regression like proportional odds model (for ordered outcomes where no equal spacing between groups without/do not need to make assumption of what distance there is). Not assume anything about spacing between those 4 categories. Best when ordinal phenotypes. If oversimplify as if binary outcome instead of 4 levels the number would be 1/15 as number of variables in model. ie IF 30 in a cohort would only be able to adjust 2 variables”
    • Two of our 4 groups (NPDR&DME vs. PDR only) cannot ordered as initially suggested (see attached figure). I recognize your interest in reducing the use of binary outcomes, but in this case, feel it cannot be done as Diabetic Macular Edema (DME) and Diabetic Retinopathy (DR) represent two, albeit related, disease processes. I’d love to hear your thoughts. At the 7/23 Biostatistics Clinic, they suggested splitting this analysis into two variables:
      • i. 3 categories- no DR vs. Non-proliferative DR (NPDR) vs. Proliferative DR (PDR)
      • ii. 2 categories- no DME vs. DME


  • 2. “Get as much info from icd 9 and 10 as can (mild, mod, severe if coded as such), Then will have manual curation (RH brought up challenge of subjectivity if 5 doing this, DAPC thinking ideally one person can be the master curator). The sd creates binary decisions of icd9 in its design(ie if captures a group with fair sensitivity and specificity, will assume that all belong in that group, but will not specify the sen'y and spec'y of that classification, as much as we can give a a score to our assumptions of phenotype, then it would be easier to rank) In analysis should have these 0.9, 0.85, 0.4 (instead record how certain you feel the dx is instead of says cohort 1, 2,3 4 is what they belong to. SHOULD ASSIGN A VALUE OF CERTAINTY. Most importantly DIVIDE Into finest gradation that can get from the data, Don’t call it a phenotype but set of conditions, If in data can break these 4 to 11 then break them up For analysis purpose and focus the power to detect snp associations. CAUTION: Break each component into more than + or – in a cohort. If breaks down within cohort but not between cohorts this method could backfire…good that we are upcoding. Some studies what they do is get description of each case and put in index card, then have someone to rank them 1-250 then analyse subjective rankings as outcomes; come out with as fine grading as can to increase power while still feasible for student s to extract. If use those rules from available data, maybe can end up being 6 vs 4 levels”
    • Can we review these processes in greater detail? I was not present for this first Biostatistics Clinic session, and so, have trouble imagining the manual curation, the interpretations of ICD codes, and the 1-250 subjective rankings as outcomes. We have not been collecting the ICD codes in the figure above as of yet, as we have noticed that coding is sometimes inconsistent and incomplete. Thus, we have been manually extracting clinical histories through chart review in the SD. However, if there is a useful application of the ICD codes, they can be easily extracted.
    • With the 15:1 ratio of patients to parameters/coefficients, whereby parameters = categories – 1, would ranking 250 patients into 11, 6, etc. categories only work to decrease our power and increase the necessary sample size? Determining how many patients we need to extract data from will be pivotal in structuring our workflows, and potentially, needs to pay for more genotyping of more patients.

Clinic Notes:
  • 15:1 rule - 1 predictor parameter for every 15 occurrences of the outcome you have in the model.
  • Case-control study makes more sense here due to budget concerns
  • Think about how covariates come in to play when sampling from SD
  • Check if department has collaboration plan with biostats
  • Would probably look at most 30-40% of data as controls - don't want a lot of "ties" in the data (records with the same value for a variable)
  • How to rank 1-250? Good for proof of concept things but hard to reproduce - you ask the experts to break the ties.
  • If you have a bunch of categories with clinical consesus of order, use that as it will give you more power.
  • Possibly rank SNPs by how they seperate across the cateogries
  • Do not do power calculation for testing hypothesis, do sample size to estimate something (ex. correlation coefficient, rank)
  • - chapter on challenges on data/power calculations
  • Check in to applying for VICTR Award for biostatistics support (90 hours): application website ( and research proposal template (


Hamilton Green, Pathology, Immunology and Microbiology

I have currently collected E. coli clinical isolates that come with deidentified patient records (excel sheet). I am interested in a subset of these clinical isolates and correlating patient records with these subset of isolates. Therefore, I want to determine if these subset of isolates derived from a targeted patient population. Mentor confirmed.

Clinic Notes:
  • Q: How to further characterize e-coli isolates - is there a statistical way I can put these isolates in target phenotype group? Is there a particular patient characteristic that these emerged from (ie. can phenotype be predicted)?
  • Note that these type data will have a lot of noise, which will hamper our analysis--must keep this in mind as we proceed and interpret. Per visit analysis--patients may be represented multiple times, but we can't tell the ID of individual patients (limitation of data).
  • More exploratory. Trying to find connections of isolate and clinical data. Need clear direction for this study--define question, future direction.


Wael Alrifai, Pediatrics/Neonatology

We used heuristic methods to create three decision trees models to assess discharge readiness from NICU: “Early model” is expected to be the first to predict discharge (potentially within 5-6 days prior to discharge), but have the highest model failure. “Late model” is expected to predict discharge last (within 24 before discharge) but have the highest consistency. “Intermediate model” is the third model. The goal of the study is to compare the performance of the three models. We would like to use the biostats clinic in defining our primary outcomes.

Clinic Notes:
  • Specificity/Sensitivity is for retrospective analyses (not prospectively actionable)
  • Don't think about how often something triggers, think when can it detect something and what probability can it assign to the child.
  • Regression trees are only useful if you have nothing but categorical variables and a very large sample size (trees are very unreliable and not reproducible).
  • Create point system for the characteristics - certain characteristics should be given more weight.
  • Frame it as prospective and think about probabilities for individual children.
  • Rethink project (from prospective view) and come back to clinic.
  • Chris Slaughter works with Neonatology, Pediatrics also has biostat support.

Matt Shotwell, Biostatistics/Anesthesiology

Retrospective study using large data set from consortium. Potential competing risks. We’re considering switching from binary endpoints to time-to-event endpoints (where possible), and summarizing using cumulative incidence curves. Seeking other thoughts.

Clinic Notes:
  • Retrospective, large database, want to know how oxygen exposure in OR effects downstream outcomes, analysis plan is mainly logistic regression
  • Instead of logistic regression - do time to event analysis, survival analysis, competing risk?
    • Competing risk models are easy to do but hard to interpret. Used for terminating events. Was not meant to be used when something interrupting the event is not fatal. Can only use 1 event. Clean interpretation can only happen if you count the event or any event worse than that as the event (possibly use this for sensitivity analysis).
  • Since you don't have timing information, if you ignore LOS and say we want a model for what happens during hospitalization - could do ordinal model that has a place for all those individual events. Need clinical consensus on order of events.

Attendees: Frank Harrell, Thomas Stewart, Heather Prigmore, Matt Shotwell, Dale Plummer


Reena Jayani, Hematology/Oncology

I am investigating the risk of toxicities of older adult recipients of allogeneic hematopoietic cell transplant compared to younger adults. Toxicities will be evaluated as a composite score, and subsequently by individual organs. My question is regarding the best way to evaluate difference in toxicity by age. Typically, arbitrary cut offs are utilized, but age may be better evaluated as a continuous variable. Would utilizing cubic splines be helpful in this protocol?

Clinic Notes:
  • Q: How best to evaluate outcome, how to treat age (cont v categorical)? Treat toxicity as ordinal outcome utilizing all organ-specific and use proportional odds model. Would model age as continuous with cubic spline at least 5 knots (can always add more at specific ages if need be, choose knots at ages that are clinically significant).
  • Q: What would be role of cubic splines, help with understanding them? Do not try to interpret coefficients, interpret the function/picture itself. Splines are effective because the relationship in the underlying biology is probably smooth (increases over time). They allow you to show the true, smooth underlying relationship between the 2 variables.

Possible analyses: Since you have yes/no for each type of toxicity - Given toxicities, can we predict age?
  • Looks in to if organ specific toxicities are likely to impact a certain age
  • If outcome age is ordinal - proportional odds model, if continuous - ordinary regression


Ali Manouchehri, Medicine - Cardiovascular

This is a disproportionality analysis study based on the reports of adverse events (AE) of drugs within VigiBase, the WHO global deduplicated database of ICSRs (individual case safety reports), originating from 134 countries as full members. Uppsala Monitoring Centre (UMC) is assigned by WHO to run and manage this registry of drugs associated AE that contains more than 20 million of ICSRs (by January 2020) since 1967 that have been submitted from clinical trials, pharmaceuticals, and national pharmacovigilance agencies. Thus, the sources of these reports can be from physicians or other healthcare professionals, to patients as the end users. The use of confidential electronically processed patient data was approved by the Vanderbilt University Medical Center institutional review board (#181337). Procedures. This observational retrospective study included all CV-AE classified by group queries according to the Medical Dictionary for Drug Regula- tory Activities (MedDRA) (Sup. Table 1) between inception in November 14, 1967, and January 1, 2020. CV-AE specifically considered in the analysis were suspected to be induced by ponatinib. Each report contains general administrative information (reporter qualification, date of reporting, country of origin), patient characteristics (age, sex), drugs (indication for the drug, dosage regimen, start and end dates, route of administration), and reactions/events (reported terms, MedDRA classification terms, onset date, end date, seriousness, and final outcome). I already have some sort of statistical analysis but need to find different or more complex analysis to make it a good fit for publication in mid or high rank journals. Also I have q about one of the analysis I made in GraphPad and Stata and I found different results.

Clinic Notes:
  • Disproportionality analysis. Make sure to get negative controls.
  • Possible violation of missing at random assumption. Imputation works when you have variables that predict missingness or timing variable when it is missing. If you do not have surrogate for the variable(s) to predict missingness, then imputation will not be well-informed/very effective.
  • Can chat with pharmacoepidemiology group (Wayne Ray, Maria Griffin, Bill Cooper)
Q: Is Kruskal-Wallis test appropriate for comparing time to event? (GraphPad, Stata results differ)
A: Could use log rank or cox model with no censoring (would be slightly better). Kruskal-Wallis is second best non-parametric test (test overall significance). Pairwise comparison - Wilcoxon Test (compares 2 groups).


Mark Does, Biomedical Engineering, and Radiology

I’m working up a plan to image (MRI) patients at different time points since disease onset, and I want to control for sex and severity of initial injury. I have a plan based on linear regression with a mix of continuous and categorical variables, and I have run a sample size calculation using G*Power. I would appreciate some feedback from an expert. This is aDoD grant to image patients post TBI to determine seizures in the future. Proposing linear regression with five covariates. Response is MRI measure of myolin (range 0-.1), possibly relatively low resolution. Looking at difference between R and L side of brain . Covariates, are time since injury, sex, severity of injury (~ 5 categories), time of epilepsy onset. Proposed sample size is ~50.

Clinic Notes:


Jeremy Hatcher, Vanderbilt School of Medicine

We are using a national database (National Electronic Injury Surveillance System) for the following: Our primary outcomes are to evaluate trends in injury count due to each gun type (non-powder gun versus toy gun) in the past decade and to examine how injury types may differ with gun type. Our secondary outcomes are gun type-specific differences in age, sex, and discharge disposition. We would like advice on the type of statistical tests that would be needed for the analysis we are interested in pursuing. Mentor confirmed. Eye injuries from nerf and air guns. Evidence to suggest nerf gun injuries are increasing. Of interest: gender and patient age, and how these factors affect injury severity and injury type.

Clinic Notes:
  • Outcome (type of injury) is a check all that apply option. Suggest logistic regression or multinomial logistic regression.
  • Plan to apply for VICTR voucher, this will fit into a voucher framework. Deadline ~Early September (or as soon as possible).

Margaret Salisbury, Medicine/APCCM

This was originally scheduled 5/4/2020 but I had to re-schedule. The study will measure markers of environmental exposures in plasma, and determine the relationship between these markers and pulmonary fibrosis outcomes in a large cohort of kindreds with familial pulmonary fibrosis. I would like assistance developing and writing the statistical methods section for a proposal to have existing samples analyzed in an NIH core laboratory (HHEAR program). VICTR Biostatistics voucher.

Clinic Notes:
  • Feedback: Advise to not use p<0.05. Will be limited by sample size for splines and interactions and clustering. Rather than AUC, suggest liklihood ratio from the logistic regression model.
  • Re: ViCTR voucher. Biostat support should come from NIH grant, VICTR may not fund. Could reach out to VICTR--ask about specifics of this case, with the specific nuances.


Meredith Campbell - Neonatology (extra-clinic consultation)

Among those born prematurely, extremely low gestational age neonates (ELGANs; born at or before 28 weeks gestation) are at risk for significant adverse consequences of premature birth, including bronchopulmonary dysplasia (BPD), the most common lung disease of infancy. Prematurity is associated with changes in lung development, specifically decreased alveolarization, and decreased pulmonary vascular growth, which increase the risk of pulmonary hypertension (PH) during infancy—particularly among those with BPD or other neonatal insults.1 While not all ELGANs develop BPD, there is a growing understanding of the impact of prematurity upon the airways throughout life; however, much less is known about the cardiopulmonary consequences of premature birth beyond infancy. A central problem in the field is a lack of understanding of the cardiopulmonary morbidity of extremely low gestational age neonates (ELGANs) beyond the neonatal intensive care unit—into childhood and beyond.

We propose to expand our studies of former ELGANs to test the central hypothesis that premature birth perturbs normal cardiopulmonary development, resulting in detectable RV abnormalities during childhood, and that those ELGANs with BPD have an even higher risk of childhood RV abnormalities. We propose to apply novel quantitative techniques to evaluate the cardiopulmonary system in a large group of previously unstudied ELGANs with and without BPD.

Clinic notes:
  • Project judged appropriate for a VICTR grant for biostatistical support.

Laura Baker - Dermatology

Previous clinic sessions: 2020-04-27, 2020-01-06

Clinic Notes:
  • Q: What is something to describe spaghetti plot to make it easier to interpret? A: challenge is growth curve ends at random point in time, could use generalized least squares (look at F. Harrell's course notes), if death is common can also have it as an ordinal longitudinal analysis where death is worst outcome (surface area).
  • Q: How to check if model is stable? A: Look at generalized R-square (most powerful way to do it) but not easy to interpret. Could also use c-index, pseudo r-square, model chi-square.
  • Q: How to approach convergence issue? A: This is more of a data issue, once you get below certain number of events the ability to do an analysis is severely restricted.
  • Q: Is there a way to evaluate how much of the variability in survival is explained by covariates? A: The assumptions made in cox model are not testable due to current sample size (# of events). Just have to make them.
  • Q: Can c-index be used to compare models? A: Not a sensitive index, it will miss real differences. Rank measure is not meant to be compared, good to look at for individual model.
  • Helpful Link:


David Isaacs - Neurology

Hypothesis 1: Patients with Tourette syndrome (TS) and obsessive compulsive disorder (OCD) have sensory processing dysfunction. Hypothesis 2: Patients with both TS and OCD have more profound sensory processing dysfunction than patients with only one of these diagnoses. Methods: The Sensory Gating Inventory (SGI) is a validated self-report measure of sensory processing dysfunction. The SGI, along with other self-report clinical rating scales (quantifying tics, OCD, anxiety, depression, and quality of life), were completed online by 3 groups of individuals: those with TS (n=15), those with OCD (n=30 individuals), and healthy controls (n=80 individuals) with no reported neurologic or psychiatric history. Primary outcome of interest: SGI score Statistical questions: – Given the small patient sample sizes, I would like to use non-parametric, robust statistical testing to compare SGI distributions between the 3 groups. Specifically, I am considering using bootstrapped ANOVA or 20% trimmed mean comparison. Does this approach seem appropriate? I have a fair amount of experience running statistical analyses in STATA, but even after consulting the STATA manual and reading various online forums, the methods for applying robust statistical approaches to this data-set remains unclear to me. – I am continuing to recruit TS and OCD patients, but if the samples sizes remain unequal, does this influence the non-parametric approach outlined above? – Lastly, given hypothesis 2 (that SGI score is more abnormal in those with both TS and OCD as opposed to those with either diagnosis alone), I would like to perform a secondary analysis comparing SGI score between the 3 groups but treating DOCS score (an OCD self-report measure) as a covariate. In a parametric context, I would approach this with an ANCOVA, treating SGI as the dependent variable, group as the independent variable, and DOCS score as a covariate. However, I am struggling to identify a non-parametric equivalent to the ANCOVA.

Clinic Notes:
  • Show distribution of score between the 4 groups using histograms, if clear differences in groups they will appear in visual displays
  • Non-parametric hypothesis test - Kruskall-wallis test
  • Another option is ordinal (logistic) regression - score as outcome, group as covariate
  • Due to amount of scores (0-36) it is possible to treat it as continuous

Lucy Spalluto - Radiology (CANCELED)

Tracking gender trends in diagnostic radiology residency programs over time. Have data on number of men and number of women in each program by year from 2008-2018. Want to analyze by higher tier programs and lower tier programs before and after 2014 (diversity programs implemented). Have draft of paper if would be helpful for review.


Jordan Wright - Internal Medicine/Div of Diabetes, Endocrinology, and Metabolism

We are evaluating retrospective abdominal CT images to determine if pancreas size differs between patients with immune checkpoint inhibitor-associated diabetes, and those without diabetes. We have performed similar analyses in patients with Type 1 diabetes and have known standard deviations and pancreas size measurements in normal and diabetic patients. I would like help in performing a sample size calculation based on these known parameters to help in application for VICTR funding. VICTR voucher request. Mentor confirmed.

Clinic Notes:
  • Without having sources of variability (age, height, etc), cannot take current sample size very far.
  • Suggest using simple regression model predicting log of pancreas volume from log of height, log of weight, age and anything else that is structural. Want to know what is relative contribution of those items on volume.
  • With items that you normalize by taking ratios it is best to use logs. Will get confidence interval for difference in logs and when you anti log result you get the full change margin of error.
  • Possibly state question as: how much bigger is the pancreas volume in one group than the other. Moving toward differences makes it more of an estimate problem not hypothesis. Asking matter of degree (precision) generalizes the hypothesis test approach.
  • Comparing before and after treatment so patient acts as own control improves power.
  • Use diabetes as continuous scale instead of binary (no,yes).
  • Sample size notes section 5.9.2 - formula for pooled variance in section 5.9.1 (

Elise Reid - Internal Medicine/Gastroenterology

Looking at the effect of health literacy and rural/urban living status on short term liver transplant outcomes. Mentor confirmed.

Clinic Notes:
  • Statistical models: Ordinal regression model, cumulative probability with logit link.
  • Outcome: Creating a hierarchical system (ordinal scale) for grading how much medical care is needed reflecting severity of patients conditions would give more signal in data. Do not want to sensor on death. Want to count death as worse than all other things. Death should be included in outcome otherwise interpretation is difficult. Need outcome that does not get interrupted by other outcomes (ex. infection could get interrupted by death). Example outcome: count number of days in hospital, rank death higher than those numbers. Talk to clinic experts to provide insight on ranking scales.
  • Predictors: Recommend using health literacy as continuous variable and putting it in regression model as 2 degree polynomial. Include interaction of health literacy and rural/urban.
  • VICTR Award for biostatistics support (90 hours):


Laura Baker - Dermatology

We are studying the reproducibility of a skin measurement device (Myoton) across 3 observers. We have performed U-statistic analyses and calculated MDC95s from ICCs. We observed that the MDC95s were generally twice the value of the U-statistics. We would like help with interpreting and clarifying this observation for our paper discussion. Mentor confirmed.

Clinic Notes:
  • MDC95 - typically useful for doing power calculation to say what difference you would like to detect, not something used in decision making (raw measurements are best)
  • Wanting better understanding of how U-statistics (pairwise difference) relate to MDC95 and ICC calculation - Likely a scaling issue, factor of 2 is built in to MDC95 not in U-statistic
  • Suggestions/Notes:
    • Reference the way U-statistic is calculated is more akin to SD unit than a 95% confidence interval, MDC is aimed at 95% CI
    • Responsiveness index might have too many assumptions
  • Reporting Options:
    • Remove 1.96 from MDC95 and redo calculation (add another column)
    • Only report what ICC is, what SD is, and the different correlations

Ivana Thompson - OB/GYN

Compare the # of (preg) deliveries/year to # terminations per year over 20 year time period. Q1: Is there a relationship b/w the two? Q2: Is there a significant change in either outcome at two specific points in time? I need help with developing an analysis plan. Independent investigator.
  • Preparing to submit IRB for ecological study and would like help on how to do analysis for project.
  • Looking at 1) number of deliveries and 2) number of terminations at Vanderbilt, by year, over last 20 years. Want to see relationship between the two over time. Also planning to look at the breakdown of type of termination procedure over time. Project aims to grow OB volume as a department.

Clinic Notes:
  • Suggestions:
    • Recommend using quarterly and cumulative counts
    • Plot data and look at correlation (number of deliveries by number of terminations)
    • Plot curves over time (1 line for delivery, 1 line for terminations)
    • Plot curves over time (1 line for each procedure type)
    • Reference points for external sources - any significant events (legislation, clinic closing, etc.) notate at time on plots


Kayla Anderson - Human and Organizational Development

I am working on interpreting results from a logistic regression in R. The regression looks at the impact of different types of capacity and other system characteristics on the presence of drinking water violations at a community water system level. I need assistance in interpreting the results and knowing the best way to present them in my Thesis. I can send you the R Code and table output, and will send you my preliminary write up prior to the meeting. My Mentor, Yolanda McDonald will be attending the session with me.

Clinic Notes:
  • Community water system survey - over 3 year period (2017-2019)
  • Most water systems don't incur violations
  • Chose to use 3 year period to expand possibility of violation occurring - could occur based on staffing changes, etc.
  • Dependent: 1. Total violations (combination of MR & HB) 2. MR violations 3. HB Violations (all 0/1)
  • Concern: long term period only looking at first violation in the period
  • Recommendations:
    • 1. Use ordinal logistic regression to account for repeats (count of violations) or have smaller time intervals (monthly, quarterly) - repeats will be captured in multiple periods
    • 2. Change "Total Violations" to "Violation of any type"
    • 3. Anti-log results
    • 4. Highlight confidence intervals of OR instead of p-values
    • 5. Possibly look at how long in between each violation (inter-violation time)


Michael Freeman - Otolaryngology - Head and Neck Surgery

National Care Mapping of Head and Neck Surgical Procedures Using NIS data

Clinic Notes:
  • Most interested in NIS (National Inpatient Sample); sample is a probability sample with unequal probabilities
  • Outcomes analysis is secondary for now, primary interest is in utilization and access
  • Population receiving procedures tends to skew towards older
  • Can match diagnoses to procedures for those diagnoses
  • Ambulatory surgery is an a separate database SASD; Sharon has used these state-specific and has data from 4 states
  • Sharon had 1998-2008 NIS data; Tom reported that Neurosurgery (Peter Moroni) has data until 2016; also check Russell Rothman/Amy Graves; also David Penson
  • New users at VUMC can sign a document to get access to data already at VUMC
  • Data years needed depends on how rare procedure is (possibly a couple years needed)
  • Most recent (2012-) NIS does not have hospital identifier; would know state and region (4 regions)
  • 2015 is when ICD9 switched to ICD10 codes
  • AHRQ advises against using NIS for state-specific statistics and recommends using SID; NIS can still make general understanding of trends (by region)
  • Ideally want database of surgeons with information about where they practice and procedures performed
  • What about CMS data? (can only get about 10% sample of data now, have to specify codes upfront)
  • These types of projects are typically best utilizing inter-department resources (find out what bigger groups have already done, what has been purchased, see if you can pool resources)
  • CMS (research identifiable files - carrier files - 2015-2016) is probably better for granular analysis, NIS for more general information
  • For HCUP (NIS data) - can query database ""


Katherine Bachman

Dr. Bachman received a VA career development award that needs to be reworked because the study drug has been discontinued. She is seeking study design advice for the revamping of her study. Here are the key points of her study proposal
  • There is growing interest in developing interventions that promote beiging of white adipose tissue in order to increase energy expenditure and possibly promote weight loss in obese individuals. Moreover, because beige fat dissipates energy as heat, an increase in beige fat could lead to increased utilization of glucose and lipids, and thus could have metabolic benefits such as increased insulin sensitivity.
  • We will enroll 100 adult men and women (ages 18-55 years) without major medical problems. We will recruit approximately 50 lean subjects (18.5≤BMI<25 kg/m 2 ) and 50 otherwise healthy obese subjects (BMI≥30 kg/m 2 ).
  • Aim 1: Determine whether beige adipose tissue markers differ between obese and lean individuals.
  • Aim 2: Determine whether beige adipose tissue markers and energy expenditure are associated with natriuretic peptide receptor expression in adipose tissue and circulating natriuretic peptide levels in humans.

Clinic Notes:
  • Project was deemed eligible for a VICTR grant for biostatistical support


Margareta Clarke - Neurology

This is a return visit from last week (3/9). Last weeks notes: Pilot study comparing quantitative values obtained from MRI scans between two groups. Data will be sued to perform power calculate for a full-scale study. VICTR voucher, mentor confirmed.This is a pilot study using MRI images with contrast in patients with MS and healthy controls. Hypothesis is there will be increased permability of the BBB in patients with MS. Outcome measure is mean of hundreds of measures. We could calculate mean and median and SD--see what measure would be best and assess the measure over time to assess the stability of the process. Suggest that the a pilot study should confirm feasibility and document obseved variance--not assess a difference. Please come back to clinic with changes and for more feedback. Also see:

Clinic Notes:
  • Random effects=subjects. One aim is to determine individual variability for each subject. Model time trajectory using flexible technique. Time to peak is the most important measure. Model time flexibly using e.g. restricted cubic spline, adjusted for random effects, and estimate the average time trajectory. Each subject will have their own intercept.
  • Try a variety of within-subject summary statistics e.g. mean, median, 0.1 and 0.9 quantiles, SD or Gini's mean difference, mean absolute successive difference, slope from a linear spline fit per subject with a single knot at zero (infusion time).
  • An exploratory analysis use the summary statistics to predict group membership using a binary logistic model. Do a chunk test for overall association, and if "significant", drill down to see what are the apparently biggest predictors of group. This is a univariate way of doing a multivariate model.


Margareta Clarke - Neurology

Pilot study comparing quantitative values obtained from MRI scans between two groups. Data will be sued to perform power calculate for a full-scale study. VICTR voucher, mentor confirmed.

Clinic Notes:
  • This is a pilot study using MRI images with contrast in patients with MS and healthy controls. Hypothesis is there will be increased permability of the BBB in patients with MS. Outcome measure is mean of hundreds of measures. We could calculate mean and median and SD--see what measure would be best and assess the measure over time to assess the stability of the process.
  • Suggest that the a pilot study should confirm feasibility and document obseved variance--not assess a difference.
  • Please come back to clinic with changes and for more feedback. Also see:

Jeff Hills - Orthopaedic Surgery

Evaluating predictors of Proximal Junction Kyphosis after adult spinal deformity surgery. Outcome: ISSG PJK severity scale (ordinal outcome, 3 levels). Predictors: Charlson comorbidity index, bone quality, preoperative spinal alignment measures, and postoperative spinal alignment measures. Adult spinal surgery patients. Goal: be able to create a better surgical plan to prevent this issue. Outcome measured at ~ two years. Longitudinal ordinal response data. Evaluating predictors of Proximal Junction Kyphosis after adult spinal deformity surgery. Outcome: ISSG PJK severity scale (ordinal outcome, 3 levels). Predictors: Charlson comorbidity index, bone quality, preoperative spinal alignment measures, and postoperative spinal alignment measures. ~145 patients, about 45 had a score, the rest scored as zero. Score categorized from original ordinal scale because some levels had no/too few cases.

Clinic Notes:
  • Would suggest repeating analysis completed using all variable levels. Suggest using all data, with multiple outcomes per patient (long file), and use measurement time as a covariate. Account for these data with cluster variance sandwich estimator (robcob). Bootstrap estimates for fit. *Come back to future clinic if needed


Trevor Stevens - Neurology

*It is known that phosphorylated tau and amyloid beta have been found in the plasma, bronchoalveolar lavage fluid, and in the CSF of critically-ill ventilated patients with bacterial pneumonias from staphylococcus, pseudomonas, and klebsiella. However, large trials have not been performed looking for an infectious proteinopathy in the plasma of severely ill septic patients. We propose to collaborate with the University of South Alabama to investigate the plasma of septic patients from the MENDS2 trial for phosphorylated tau and amyloid beta. Questions: Using plasma from septic patients, will there be elevated levels of phosphorylated tau and amyloid beta, and if so, will there be endothelial dysfunction caused by these infectious amyloids? Further, using statistical analysis, will elevated partial thromboplastin time be associated with elevated levels of amyloid beta and phosphorylated tau? VICTR voucher, mentor confirmed.

Jonathan Siktberg - VEI

Our project is a case series of 653 patients who have been treated for age-related macular degeneration under the step therapy protocol at VEI. We have completed data collection and preliminary analysis. We would like guidance on our analysis to confirm the statistical tests we are running are appropriate. Third clinic, mentor unable to attend.


Michelle Griffith - Medicine/Endocrine

  • I’m submitting a VICTR resource request. Project will include RD custom data pull, and I have that quote ready. I would like to include biostatistics request on same application.
Project: I will use the RD to create a data set of telemedicine encounters (defined by visit type and billing modifiers) in endocrinology, and a comparison set of control office visit encounters. Data will be pulled at a ratio of 3 controls: 1 case , matched on provider, primary cpt code and primary icd code. We aim to provide descriptive data about the types of conditions being cared for on telemedicine and begin to assess for differences in utilization of imaging, lab, between these two types of encounters. We will also assess for differences in clinical outcomes for conditions where those are well established (eg most recent HbgA1c in paitents with diabetes.) *These are data from the RD for patients with DM and other endocrine disorders with face 2 face and telemedicine visits. These are matched on diagnosis and provider. The subjects can move between groups. The question is does resource utilization change with telemedicine visits? The issue with this study is potential confounding. We can’t tell if the differences are due to disease or visit type. Outcomes of interest are number of visits, medications, labs, etc). It’s likely that these data may not be able to be used to drawn meaningful conclusions. One option could be to attempt to do a secondary analysis of a larger RCT. Could apply for a voucher, even though at this time it may not be needed. This project would be appropriate for a voucher. Tom Stewart would be contact for analysis plan review.


Heidi Silver- Medicine/GI

  • To determine HRs for % increases and decreases in skeletal muscle mass and density - with survival as the outcome. The assumptions involved in this question are large, and may not hold up if studied in a larger study. Suggest looking at alternate data sources to study change over time. One option using the current data would be to describe the shape of the relationship.

Gianna Petrelli- Master of Genetic Counseling - VUSM

  • I would like to address if the demographic variables of age, race, ethnicity, income level, preferred language, and age correlate with acceptance to the Undiagnosed Diseases Network (UDN). I have little to no background in biostatistics so I am hoping to discuss what statistical analysis would be best to perform as well as the methodology of selecting subjects. There are over 300 applications, so I’m wondering if it would be best to include every application in the study or if I should randomly select applicants (and if so, how should I do that?). VICTR voucher/mentor confirmed. Masters project (no voucher support), may return to clinic as often as needed.Best approach for these data may be descriptive. Descibe differences, and describe relationships between vaariables. Graphical means prefered.


Michael Lowry- Infectious Diseases

  • We are looking to assess the rates of infections in persons who inject drugs in Tennessee, compared to the nation as a whole. We are using HCUP data and plan to use TDoH HDDS as well. VICTR Voucher, Mentor confirmed. Study suitable for voucher. Analysis of infection rate in the state of TN.

Yvette Ssempijja-Dermatology

*Reliability (ICC) and inter-observer disagreement between 3 trainee groups. Mentor confirmed. 1. Thoughts on the statistical report card used to compare new users to experienced measurers 2. How best to compare the groups? (mean pairwise difference) 3. Presentation of data

2020-2-4 (Extra-clinic consultation)

Alex Langerman and Michael Freeman (ENT)

*Our project seeks to leverage hospital billing data to find out more about surgical care patterns nationally in head and neck surgery. There are many procedures in ENT that are only performed by fellowship trained providers in few geographic areas, and this holds implications for patient access to care across much of the country. We are going to take surgical billing data from the National Inpatient Sample (NIS) and use that to look at a handful of head and neck surgeries (free flap reconstructions, total laryngectomies, speech prosthesis placement) in terms of where and how often they are done. We can then use other population-scale data from the american community survey to deduce care access metrics like distance traveled for care. *Questions, 1) where are these specific procedures done, and how often are they done at each location? 2) How far are patients traveling for these procedures and are they traveling? *The ideal dataset for these questions would be one with procedure codes (CPT/ICD10), diagnosis codes, hospital identifier and patient zip code (or location). Could use use NIS dataset, perhaps with SEER data for context.


Shawniqua Williams Roberson- Neurology

*Pilot project examining a measure of EEG complexity (called Approximate Entropy) as a potential biomarker for cognitive function. Would like to vet basic statistical approaches to compare approximate entropy with dichotomous and continuous clinical variables in preparation for upcoming VFRS submission.
  • K23 with June deadline; will likely return with VICTR voucher request
  • technical issues: (1) age normalization of RBANS will be inconsitent with the fact at EEG is not normalized. solution: direct adjustment for age.. make age a part of the model. (2) make magnitudes key quantities of interest; not whether it exists or not. (3) mean absolute difference from one difference to the next, as measure of entropy; or just multiple measures as opposed to one measure; which sombo of summary measures is the most predictive?

Wonder Drake- Medicine/Infectious Diseases

*I am interested in a clinical trial of metformin for sarcoidosis patients. I have in vitro data of efficacy using their T cells. I want to propose to try metformin in a Phase IIB analysis. I need to know based on the in vitro data, how many subjects do I need to enroll in order to have sufficient power?
  • data is longitudinal, so need to consider within-patient correlation: baseline & 6 weeks (+/- 7 days)
  • simplest design: within-person change on some marker. everybody gets the drug at baseline and evaluate difference at 6wks
  • parallel-group design: baseline should cancel out and look at 6week value of controls vs 6week value of test group, controlling for baseline values.
  • will return to clinic with previous study's standard deviation of CD4 to help estimate sample size required.


Gabriella Glassman - Plastic Surgery

  • myoton lymphedema device comparing device ability to differentiate levels of stiffness etc in arm with lymphedema and without lymphdema
  • myoton handheld device - looking at lymphedema patients after breast cancer. skin stiffness/muscle stiffness/creep/frequency; lymphedema in one arm typically; n=11 patients; high noise in the device; possible skew in some area measurements
  • recommendations: rather than taking ratios, use the differences of logs of sums of arms and performing nonparametric test; OR, don't transform: use difference of sums of arms and then perform nonparametric test (to help with interpretation)
  • pearson correlation: in skin v muscle, beware of within-patient correlation. could average the correlation over each patient.
  • suggestion: scatterplot of affected vs nonaffacted for each measurement (x=unaffected , y=affected) --> this will help decision of whether to transform
  • other study: ambulation study with fitbit; 2wks prior to surgery and 8 wks after surgery; RQ: when do women return to baseline after surgery? Q1: when do you define returning to baseline? hitting once, or hitting and maintaining? Q2: how many patients?
  • ratio may not be best way to look at this data. (it means something different for an inactive person to double their steps than for an active person to double their steps; ratios eliminates that baseline activity level information)
  • consider looking at a one-week average of scores (will be more stable), rather than individual days); calculate 7-day moving average and don't consider them returned to baseline until they've returned to their pre-surgery average); there is too much variation in indiviual days
  • analysis: for each patient, calculate number of days to return to baseline; then you can use that measure to run analyses; sample size will depend on variability and how long you can follow up patients (ideally to return to baseline or until you're confident they wont return to baseline)

2020-1-17 (Extra-clinic consultation)

Patrick Kelly - Neurosurgery

  • The project is about whether a particular lab value (red blood cell distribution width, RDW) improves prognostic modeling for patients with glioblastoma multiforme. We have preop and multiple postop values for each patient, as well as the most important clinical variables predicting survival in this population. The main study question will involve a chunk test to determine if this lab contributes to predictive modeling. We also want to create a spaghetti plot of the lab values over time, and assess the trend in the lab value leading up to time of death.
  • Project is eligible for a VICTR grant for biostatistical support
  • Estimate of 40-80 hours required.


Stu Reynolds/Siobhan, Urology

  • RQ: how restrictions on work affect how they use restrooms -> urinary symptoms
  • survey: pilot study done over 14 days / good response / recall data
  • analyses: individual level and across groups; 5 groups of subjects, within hospital
  • will likely return to clinic; considering VICTR voucher

Jeffrey Hills, Orthopaedic Surgery

  • review of study; external validation


Laura Baker - Walk-in

  • Research Q: whether change in body surface area (%) between visit 1 &2 is correlated with survival.
  • Question for clinic regarding units on hazard ratios.
  • Looking at: (1) change in bsa between visits - percentage scale; (2) percent change in bsa between visits - will have more resistance accepting this measure
  • Recommended to use log BSA1 and logs BSA2 in the model instead. Put them both in the regression model, rather than using their difference. (The different visits are weighted equally when you combine them into one variable.)

Alvin Jeffery - Nursing and Biomedical Informatics

  • We are conducting a secondary analysis of data collected regarding adherence with an ICU care bundle (i.e., the ABCDE bundle). In this hypothesis-generating secondary analysis, I’m attempting to use PYMC3 to develop a hierarchical regression model (both binary logistic & ordinal logistic) to determine if physical distances from some pieces of equipment are influential on bundle adherence. I took Chris Fonnesbeck’s class last Spring, but this is my first “real” analysis using pymc3. I was hoping to get feedback from Bayesian folks on whether I have appropriately parameterized my model(s). For the hierarchical component, we have multiple observations per patient and multiple patients within each of 10 units.
  • Adherence measured per patient per day, binary yes/no.
  • Currently looking at binomial model and hierarchical model.
  • More likely to adhere the longer they're in hospital.
  • Actual distance per patient not collected, so they're looking at close vs far scale, or possibly mean of longest/shortest distance.
  • Distance measured in inches...
  • Imputation: try R methods
  • Use indicator variable to absorb lack of fit of distance variable and put prior on it; use additively.

Jay Bagai - Cardiovascular Medicine

  • VA project to assess performance of stress test in patient's undergoing stress testing prior to low risk surgery.
  • Looking for recommendations on variables to collect. Hoping for n=200 patients. With so few patients, the probabilitie cannot come from own data. A published risk model will need to be found and use the variables included in those models, which has the details of coefficients and scoring rules. Then they will return to discuss analyzing probabilities and showing difference post versus pre test.


Ronnie Beaulieu, Infectious Diseases

  • Retrospective analysis of antimicrobial utilization over time relative to the implementation of a stewardship intervention. *I would like to discuss the analysis plan, and get assistance with applying to VICTR for biostatistics support.
  • VICTR voucher, mentor confirmed
  • Outcomes: total use in days of therapy (DOT) overall, and by drug; spectrum score, duration, acceptance rates, LOS, mortality, cost
  • estimate of 90 hours
  • deadline: end of June


Emily Sedillo, Master’s of Public Health

  • The project consists of doing analysis on survey data surrounding contraceptive prevalence, contraceptive type preference and attitudes surrounding contraception. The data is from Lwala Community Alliance’s “household survey” that was completed between 2018-2019 and includes survey responses from 5 rural regions in western Kenya. The overall research question is “What are the factors that influence the utilization of contraceptives in order to reduce unplanned pregnancies for women in Lwala’s catchment areas within Migori County, Kenya?”
  • VICTR voucher, mentor confirmed
  • Timeline very short, we will need information on the readiness of the dataset.
  • Estimate of 40-80 hours required. Single dataset with probability weighting.
  • Non-response likely due to sensitive data/subject.
  • Apply for VICTR ASAP and hope it's approved by end of year. If data is ready immediately, quick project timeline is likely doable. Issues with data will cause delays. Once the application is received, put in the queue for VICTR help from biostatistics. For application, put together detailed analysis plan and protocol.
  • Tables with missing data patterns, table of descriptive data, any comparisons/models requested.

Raymond Zhou, Infectious Disease

  • This is a retrospective chart review of refugee patients being treated at the Siloam Health Clinic. We motivated by a desire to improve the efficacy and efficiency of the Hepatitis B screening and vaccination efforts at Siloam and the refugee camps they receive patients from. Of note, Hepatitis B vaccination is ideally completed with multiple doses.
  • Primary questions we want to answer are whether the average number and spacing of vaccination doses differs according to various demographic variables, such as country of origin and age. Other questions include whether the average dose/spacing differs across patients positive vs. negative for HBV, HCV, and HIV.
  • Evaluate data integrity without manually reviewing each chart; what analyses are most practical and useful.
  • Categorize immunization adherence into four groups, calculate mean across variables of interest, and calculate (and emphasize) the confidence interval surrounding that mean.


Jourdan Holder, Hearing and Speech

  • Population: Cochlear Implant Recipients
  • Research Question: Does more consistent device use drive better speech understanding?
  • Question(s): We are looking to get some advice regarding a power analysis/sample size justification to address the following: We completed a correlation study in which the correlation between hours of daily device use (std. dev. = 4.2) and speech recognition (std. dev. = 22.5) was 0.6 with a slope estimate of 3.3 for n = 290. In the present study, we are interested in assessing causality (i.e., does increased device use cause better speech recognition?) via an intervention study in which the independent variable will be an increase in hours of daily device use and the dependent variable will be speech recognition.
  • For F32 grant to be submitted next week. SS calc can be simpler than actual analysis used. For SS calc, use one-sample t-test on the difference (within-person) between timepoints; or could estimate SS needed for certain CI width you'd like to obtain. Make a plot of the current patients with both timepoints existing, to give grant reviewers an idea of the data. For analysis, untransformed spearman correlation. In model, control for the average number of hours spent in quiet time per day. Also examine adherence to usage increase.


Jay Bagai, Cardiovascular Medicine

  • Previous visit: 2019-10-28
  • VA outcomes study seeking to determine the current usage and utility of nuclear stress tests for cardiac assessment prior to low-risk surgery.

Karampreet Kaur, Otolaryngology

  • This project is a retrospective cohort study of patients >18 y/o who received a tonsillectomy by Dr. Kim Vinson. Currently, no guidelines exist for when to offer tonsillectomy to adult patients. The aim of the project is to describe this patient population, analyze factors that contributed to the decision to offer tonsillectomy, and to analyze outcomes for these patients. I would like to run analyses look at indications for tonsillectomy in patients who were students vs. those who were not, and patients who were singers vs. those who were not. Additionally, I would like to look at the relationship between indication for tonsillectomy and age to see if there is a correlation. * Mentor confirmed


Jacob Beckstead, Pharmacology

  • Our interest is in determining whether metastatic progression in patients with colon cancer is associated (positively or negatively) with also having an atopic disease such as asthma. We have used the SD to identify patient groups with colon cancer (stage 2 or higher) with or without metastasis. Both control (no metastasis, n=361) and case (with metastasis, n= 906) groups have now been selected and have PPV of greater than 95%. We want to know how best to balance the groups and then the appropriate analyses to determine the association with asthma or such disease.
  • Ideally collect more patients with asthma (currently 10% of controls and 5% of cases). Include a negative control group.


Jay Bagai, Cardiovascular Medicine

  • Retrospective analysis of 1192 VA patients. We are looking at effect of transitioning from trans-femoral (TFI) to trans-radial PCI (TRI) in 2009. Prior to 2009, pts underwent mainly TFI and after 2009, pts underwent mainly TRI in years 2010-12. The problem is that patients were not randomized to either group, and we cannot compare TFI and TRI groups in 2006-2008 as there were too few TRI and in years 2010-2012 as there were too few TFI. In 2009- the transition year, comparisons are also difficult since pts were not randomized.
  • To determine variables to inlude in matching: assemble group of experts and determine which variables are required for a person to be assigned femoral or radial (without telling them which variables are already included). will reinforce variables included in model. In the model, interact calendar year with treatment. Cumulative volumes by operator are the stronger indicator of learning curve over time. Ways to proceed: (1) return to biostats clinic, (2) cardio collab plan, (3) VICTR voucher, (4) VICTR studio.

Jessica Kneib, Pathology/Transfusion Medicine

  • This project will look at the variability in ECP procedure costs across the country. We have collected data including chargemaster list prices, facility type, location (urban/rural), hospital referral regions and hospital size. We would like to determine of any of these factors are associated with variations in procedure cost.
  • variability in cost of one procedure across hospitals/providers (n=92). For graphics, use histograms with bins, as to not display individual hospitals. Model the prices with related variables; but use a robust method that can deal with outliers. Investigate the non-response bias.


Eva Mistry, Neurology

  • This is a prospective case-matched control study to understand the treatment effect of endovascular therapy in patients with vs without pre-existing disability. My specific questions are: (1) What are the minimum number of controls do I need per case if I were to match for x number of baseline characteristics? I also need help with sample size estimation. (2) Can I perform ordinal regression if the two groups of patients are expected to have an unequal "starting point" across an ordinal scale. For example, I am comparing the two groups of patients on a scale that goes from 0 to 6 at 90 days, but one group, at baseline as a score 0 or 1 and the other group as baseline score of 2-3. So at 90 days, I expect that the scores for the patients in baseline 0-1 groups will range from 0-6 but those in 2-3 group will range from 2-6. Can I perform an ordinal regression in this scenario? (3) If not, what are some appropriate alternate tests? (4) I also plan to perform cost-effectiveness analysis of endovascular treatment in disabled patients. Does this analysis require specific statistical expertise? If yes, who would be the most appropriate statistical collaborator?
  • Direct matching recommended, using all available samples. Sample size will depend on practical limitations, taking into account the variables you match on, in order to maintain a minimum saturation. Include baseline score as independent variable in model.

Alexander Hawkins, Surgery

  • Looking to assess the association between preoperative transfusion and disease free survival in patients undergoing rectal cancer resection. Favor a propensity adjusted analysis as the two groups are different at baseline.
  • For propensity-adjusted model, must have variation in the protocol for transfusion. Could model who gets a transfusion and who does not, then compare propensity scores to see how much they visually overlap. Could evaluate "dose-effect" of how much blood is tranfused.
  • Project was evaluated for a VICTR grant for biostatistical support. Statisticians agreed that the number of staff hours required to complete the project was within the standard grant limit.


Fiona StrasserKing, Cardiology

  • Descriptive study on HF in Zambia
  • HF in pregnancy prevalence and risk factors; FU for six months; screen for hf diagnosis (diagnosis usually made one month before delivery to 5 months after delivery)
  • 16% in pilot diagnosed with hf (incidence rate)
  • SS requires precision, width of confidence interval; recommended to provide a reasonable estimate of what sample size you can obtain, then estimate the precision that that sample will provide.
  • Recommend to spend more resources on aggressive follow-up and quality of study than obtaining more samples.


Monika Schmidt, Cardiology

  • Objective of the study is to examine how professional titles are used in the same mixed gender speaker introductions at national cardiology conferences.
  • Primary end point: determine whether the speakers professional title was used during introduction of presentation.
  • Secondary end point: determine whether the speakers professional title was used during anytime during the presentation.
  • Collecting race and gender of introducer and presenter; analyzing recordings of text of conferences from 2016-2019. Analysis: 1) descriptive statistics. 2) among proportion of people who use both professional title and name, analyze data at introducer-level. Notes: age-differential between introducer and speaker play's a big role. Statistical power will be driven by proportion of introducers which use both (as introducers who are consistent will not be included in analysis). Each introduction will be an observation in the dataset, with an indicator variable of same race vs different race. Analysis: logistic regression allowing for multiple rows per introducer. Include calendar time variable in analysis; look at interaction between calendar time and probability of using professional title in secondary analysis. Incorporate role (NP, MD, PhD, etc.) as well. Possibility of getting a VICTR biostatistics voucher.


Allison Wheeler, Pathology

  • I I am designing a retrospective project that will compare IUD use in women with heavy menstrual bleeding (HMB) with and without bleeding disorders. Our primary objective is to compare the efficacy of Mirena IUD as treatment of HMB in adolescents with and without diagnosed bleeding disorders by evaluating bleeding outcomes as well as to compare complications such as expulsion. Approximately 20-40% of adolescents with HBM are diagnosed with a bleeding disorder. My specific question is geared towards cases versus controls:
• Given the expectation that for every female with a bleeding disorder and HMB there will be 2-5 without a bleeding disorder, can I collect data in a 1:1 fashion or should I have more non-bleeding disordered patients in the analysis? * Could do 2:1 or 3:1 matching--not much utility after that. Could also do matching at site, or pool data and then match.


Christina King, Chemistry

  • I am interested in studying racial disparities in hypertension by comparing plasma samples from middle-aged normotensive and hypertensive African Americans. This is a pilot study that will be executed by using quantitative proteomics techniques.
  • VICTR voucher/Mentor confirmed (voucher for biostat for aim 1 & geneticist on board for phenotyping) VICTR may not like that it's already funded.
  • Specialized biostat faculty may be interested in this project, if some other funding approved. VICTR statistician needed initally for aim 1, but will need to check on how funding may work.
  • Consider definition of hypertension in study.

Naeem Patil, Anesthesiology

  • I am planning animal and clinical research study aimed at recruiting septic patients and studying certain bio markers to correlate them with disease outcome. I have questions about sample size calculations and power analysis.
  • Anesthesiology collaboration within biostat dept will give more specialized expertise than clinic. (Meeting with collab later today.)


Rajiv Agarwal, Medicine/Hematology/Oncology

  • I'm a new faculty member at Vanderbilt, and am part of the MSCI/K12 program. I'm hoping to learn more about how to design effective studies related to my research - on measuring outcomes longitudinally over time from palliative care interventions in patients with cancer.

Yuxi Zheng, Ophthalmology

  • Surgically naive students interested in going into a surgical field will perform a series of pre and post tests to assess for speed and accuracy under the microscopes in the wetlab. They will be randomized to either the 2d or 3d group and assessed on various tasks pre and post intervention.


Ronald (Ronnie) Beaulieu, Infectious Disease

  • We are interested in a time-series analysis to evaluate the impact of an antimicrobial stewardship intervention on antimicrobial utilization rates. Questions: How many data point/how much time to reach power? Statistical methods to analyze outcomes (time-series vs pre-post). Feedback adherence rates. Change in utilization/time. Mortality. Cost.
  • VICTR voucher/mentor confirmed
  • pilot study done - reviewing antimicrobial use for inpatient. time series analysis before and during the antimicrobial stewardship intervention
  • Q: What time period do we need to observe? been implemented since april twice weekly. 30-40 datapoints
  • Data: 0-18 patients per week; could look at: feedback day, weekly (2 feedback days), biweekly (4 feedback days); 5 teams: disease burden within each team is approx same
  • Analysis plan: account for confounding variables over time (institutional shifts, etc.); tracking adherence to feedback recommendations; censoring data for those who receive an infectious disease consult
  • Primary objective to look at reduction of days of therapy. Possible primary outcome: change in total antibiotic utilization (per thousand days)
  • next steps: identify patient population; visualize time trend; identify primary question that you want to answer/which outcome makes more sense to audience; check data variables that shouldn't matter (like LOS) to make sure they don’t; investigate patient-level data; state-transition model may help to visualize

David Wu, Cardiovascular Medicine

  • We are looking at protective association of lithium and cardiovascular diseases both epidemiologically using groundwater lithium and using patient data. We are looking for ways to finalize and strengthen our current results.
  • VICTR voucher/mentor confirmed
  • 1962 geological survey data (migration effect not accounted for - huge limitation) (bottled water/water filter use has changed over time) (area-level ecological analysis has its own problems, since different from individual data - subject to ecological fallacy) (try to adjust for age - mean age for country) (nonlinear adjustment for age and income)
  • current analysis: lithium rate/mortality correlation (county level mortality); regression adding income; currently assuming linearity (may be a threshold effect to account for); lithium levels transformed (sqrt, log); lithium also associated with prevalence of diabetes (unknown whether mediating or simulataneous)
  • "lithium protective for MI in bipolar patients" - need to dig into EMR (balanced levels of MI, why some pts are started on Li and others are not, etc.)
  • "lithium protective for MI in diabetic bipolar patients" - be careful with phe codes (how diabetes is denoted in SD) (hard to tease out confounding effects of diabetes)
  • confounding is so unknown, that p-value and "significance" not recommended. Use descriptive data, slopes, confidence intervals, etc.
  • for VICTR application, voucher would be to pull data from SD (need to talk to the SD folks to get details on that) (describe the group that didn't get lithium and for what reason)


Pingsheng Wu, Medicine/Allergy

  • Determine whether metabolite or combination of metabolites measured at birth can be markers of in utero exposure to smoking and even to specific product of smoking.
  • VICTR voucher
  • Maternal smoking or exposure-to-smoke (along with frequency of smoking). Data: pregnancy assessment monitoring system, surveillance of 4-6 months after delivery, assesses pre-pregnancy/in-utero environment. National survey. Proposing to use 2009-18 data, limited to 2 states, linked to newborn screening database (national, taken within 24 hours, 37 metabolites measured; standardized procedure done by state). e-cigarette usage and amount documented from 2016. hookah usage documented as well but not amount.
  • After quitting, metabolic pathway found to quickly recover. Never-smokers and those who quit before smoking have similar results. Third trimester has highest impact on child.
  • Can we identify metabolic pathways associated with effects of smoking in utero?
  • Subgroup analysis: can you identify second-hand smoking?
  • These biomarkers would have to work in a dose-response way.
  • Goal is to use biomarkers to determine whether intervention is needed (like vitamin C, shown to reverse detrimental effects of smoking on babies' lungs in utero).
  • Suggestions: Internal validation with bootstrapping. Use variable clustering to reduce 37 metabolites to ~7; tree with Spearman rho.


Emily Ambrose, Otolaryngology

  • Returning with research mentor to discuss chronic cough triage tool.
  • Lit review non-productive. Suggested by a mentor to use synthetic derivative to examine scope of problem.
  • SD contains clinical data + free text. Harvard has a guideline for cost-effectiveness analyses. Conduct a VICTR studio to assemble experts to determine clinical pathway/proper workflow. Starbrite website > funding tab > apply for VICTR studio. Specifically ask for people from certain departments.


Dave Patrick, Cardiology/Clinical Pharmacology (NO SHOW)

  • We will examine the correlation of a novel biomarker with clinical characteristics and laboratory values in patients with lupus (SLE). I am proposing to use univariate and multivariate analysis. I am preparing a grant on this topic. During the clinic, I would like to address a method for calculating power and necessary patient enrollment numbers for this project.
  • PGY7 Resident, future faculty, mentor excused.

Mike Lowry

  • We are looking at rates of serious infections in intravenous drug users during the opioid crisis. We will have discharge data from TN in the last 5-10 years that we will use. We will also plan to use nationally compiled data (discharge codes) to see how TN compares to the rest of the nation. We will look to compare the incidence of serious infections year-by-year in TN and then compare these to incidence nationwide. Our question is: what type of statistical tests can we use to properly show this?
  • Mentor confirmed and present
  • VICTR voucher request--suggest to return to clinic before submitting given suggestions provided
  • Goal is to compare trends over time in TN, and compare to national. Anecdotally seeing more complex infections in ID from IV drug users.
  • Plan is to use HEPC+ as a marker for IV drug use. This will capture some users but will also capture non users. Need to define group of interest (patients with infections or infection cases), and decide if hep c positive is a comprehensive enough marker for IV drug use. Need to also find out if data contain ED obs patients who ae d'c from ED.


Ashley Nassiri, ENT

  • Vestibular schwannomas are benign skull base tumors that have variable growth rates. Treatments include surgical resection, observation, or radiation. Because these tumors are benign, we are conservative with surgery and debulk, but generally leave some tissue behind if it is adherent to the facial nerve (which controls muscles of the face). Rather than damaging the nerve by trying to do a complete resection, we do a subtotal resection and have better facial nerve outcomes. This however may lead to future tumor growth, and we are interested in evaluating the factors associated with postoperative tumor growth after subtotal resection. We have collected tumor volumetric data (from surveillance MRIs after surgery for many years), patient demographics, and other important disease related metrics. We would like to analyze these factors to see which are associated with postoperative tumor growth.
  • Total resection or partial (to preserve facial nerve function). Baseline preop volume; and yearly follow-up, location of tumor, amount of tumor left after partial resection, demographic vars. Stable tumor size or growth after resection. Some patients have received radiation in follow-up (16/46), if tumor grows. Use tumor volume as longitudinal variable. Scan pre-op, then immediately post-op (within 12 hours), then every 6 months. Proportional scale or absolute scale? Adjust for initial volume, then capture change on absolute scale in post-op scans. After radiation, patients are censored. look at facial nerve outcome over time as well (scale of 1-6). (Facial nerve function affected by radiation.) Particularly interested in outcome at one year and what is happening within that first year. Will be applying for VICTR voucher. Two vouchers (analysis done at same time).


Emily Ambrose, ENT (walk-in)

  • Help in data collection for development of a chronic cough triage tool (questionnaire to take when setting up subspecialty referral appt). questionaire is developed and wanting to validate. purpose is to triage pts to the right clinic, in order to decrease cost and improve practice. tracking referalls would be difficult: referall comes from all over. just at the beginning of the project; to work with clinical experts in various field to determine clinical workflow.
  • VICTR support possible if project involves research (research into patient care, validating/showing impact of the questionnaire)
  • Take plenty of time to brainstorm/come up with a plan; i.e. avoid seasonal issues, find a control group. First steps: describe current workflow/current scope of problem (interview small number of patients, with clinical expert review), pilot test questionnaire
  • Come back to biostatistics clinic with your mentor when a more concrete plan is developed.


Gabriella Glassman, Plastic Surgery (Walk-in)

  • Survey results from plastic surgery programs: ~ 71 across US.
  • Recommendations: 1) Descriptive summary: Calculate distributions of responses for questions (no formal comparisons). 2) Formulate questions that you want to answer. 3) Apply for VICTR biostatistics support through Vanderbilt faculty supervisor, emphasizing why this project falls under the umbrella of VICTR ("implementation science project", looking at how training programs operate). If accepted, you'd be assigned to a staff biostatistician. VICTR voucher discussed; contact assigned faculty statistician to continue.


Stephen Gallion, Kiersten Espaillat, Neurology

  • Hypothesis: There is a positive correlation between the number of licensed county EMS vehicles per population in a given county and the prevalence of negative stroke outcomes in the same county. Secondary Hypothesis: The Social Vulnerability Index score can be a protective factor in areas with fewer trucks per population. Summary: This project seeks to analyze available data on the number of EMS vehicles, stroke patient outcomes, social vulnerability index score, and number of stroke centers in a series of Tennessee counties to identify whether or not the hypothesis (above) is supported.
  • 37 centers, county level data. Response time of more importance than distance. Group level data/ecological data tends to show reversal of data in large, urban counties
  • GIS analysis using ESRI, USGS, etc databases recommended. Small area analysis re health. Will return for clinic when a GIS expert can be present


Midya Yarwais, Pediatric Rheumatology

  • The aim of this study is to estimate the prevalence of medication non-adherence and identify demographic and disease characteristics associated with medication non-adherence in youth with childhood-onset systemic lupus erythematosus in the pediatric rheumatology clinic at Vanderbilt. Medication possession ratios (MPRs) will be calculated using pharmacy refill data for all immunomodulatory medications over a 2 year period of time to estimate medication adherence. Chart abstraction will be completed to obtain demographic and disease characteristics. We are seeking assistance from the biostatistics clinic to ensure that we collect the correct details required to accurately calculate MPRs and that we organize these details in a format that can be efficiently analyzed after export from the REDCap database. We would also like to review our planned statistical analysis to determine if it is an appropriate/feasible project for a biostatistics VICTR voucher.
  • Mental health and medication adherence in children with lupus, estimating adherence via refill status (medication-possession-ratio) need help calculating time-period. MPR calculated as total number of doses dispensed over period of time. did they fill their prescription enough (percentage); restrospective 2-year chart abstraction. Interested in examining duration of disease; are patients more adherent at the beginning of their diagnosis?
  • Limited in sample size, and therefore the number of covariates - 96 patients needed to accurately estimate possession (yes/no), without covariates, for 0.1 margin of error. Pharmacy refill data only go back 2-3 years, so limited to using current patients.
  • Suggest an outcome with higher resolution (ex: hours of gap time between prescription) or measure something more often in the same group of people (ex: blood pressure measurements every ten minutes). The outcome should be clinically meaningful, helping to advance knowledge or prove feasibility. Suggest to use two sources of data: pillbox cap detection validates easier collection of pharmacy refill data.
  • Secondary endpoint to evaluate MPR on outcome of disease index/severity, with a correlation coefficient. (Requires about 400 patients to get margin of error of 0.1.)
  • Could include young adults from young adult rheumatology. Or could try to recruit another pediatric rheumatology practice. Could include other diseases with similar characteristics. Could look at severity as time-depending covariate on adherence, after defining a good baseline. (Longitudinal data moving forward with current set-up would be an advance in current literature.
  • Think about what kind of conclusion you'd like to present, in order to determine the type of study required.


Yolanda McDonald/Kayla Anderson, Human & Organizational Development/Peabody College

  • This study (statewide survey of TN Public Water System Operators [N=3,608]) addresses the following research question: What are the current and future challenges that operators face in providing a safe drinking water supply for Tennesseans? We want to review the survey instrument (56 items) with a biostatistician to ensure that variables are optimally operationalized for descriptive and inferential statistical analyses.
  • Details: Aging infrastructure and aging workforce. Are there differences/different challenges in water quality for purpose (park, hospital, etc.), population density, education, etc.? Survey to be given to water operators. Goal of survey to address those differences, and to be used as a tool for education for the water operators, dept. of health, dept. of conservation.To be dispensed via email to the 85% who have email addresses on file and via hard copy to those who do not. Goal response rate of 80%. First time this is being done in the US statewide, so there is external interest in the results.
  • Recommendations: (1) Determine differences in those who respond and those who do not. You can include a question at the beginning asking why they do not wish to respond, if applicable. Plan for this, so that you can make judgements about the response bias. (2) Put thought into the cover letter. If there is someone they respect, a cover letter from that person encouraging them to respond could help. Often offering the results to the survey-takers is a good incentive to respond. Highlight how you plan to dispense the results to the survey-takers in the promo message. Emphasize anonymity. (3) Reformat questions: make likert questions into a matrix; change interval-scaled responses to numeric, continuous response. (5) Incentive of a raffled reward. (6) Analysis: descriptive, correlations, R package for exporting REDCap data. Means or correlation coefficients, plus confidence intervals, will be more useful than hypothesis testing for survey results. (7) Double-check with VICTR central about whether this can be funded by VICTR, emphasizing public health; future studies from this study will look at water systems and health outcomes. VICTR doesn't award grants post-grant award.


Shriya Karam, Epidemiology

  • Study on Ovarian cancer and BMI. Goal is to calculate mean and median of BMI values in each 6 month time interval from the primary cancer diagnosis date.BMI among women diagnosed with ovarian cancer. USing EHR data from the Synthetic Derivative, currently being processed. Will be using SAS for analysis. If BMI measurement around date chosen in 8-week window. All ovarian cancer cases, so assumed BMI will be measured around diagnosis to determine dose. First initiative is to try and characterize what the changes in BMI are. Accessing any record a patient has, in the whole system.
  • Can use time-varying covariates. Assumes that the BMI during the whole interval is the same. Windows are not uniform for every patient.

Jake Hughey, Biomedical Informatics

  • I’m studying the association between the presence of a preprint and the altmetric score and number of citations to the corresponding peer-reviewed article. This is an observational study, and I’d like to get feedback on my analysis and interpretations.
  • Interested in comparing metrics (altmetric score and number of citations) between papers that have a preprint and those that don't. Preprints can be updated (optionally) but are never removed. Preprints are relatively new in the life sciences. Most journals accept preprints, however some explicitly do not publish manuscripts which already have a preprint.
  • Analysis planned is regression, with log transformation on retention score and number of citations. Number of citations x preprints, adjusting for MeSH terms (assigned to almost every peer-reviewed model). Number of MeSH terms varies from one journal to another, so principle components are calculated journal-by-journal and planning to produce a different model for each journal with top 10 PCs for each model. Random effects meta-analysis model to provide aggregate estimates. Meta-regression then used. Using 4 years worth of data. Not including an interaction term between time since publication and preprint.
  • Suggest using mixed-model and simplifying the analysis. Suggest to use weight and height rather than BMI. (Data is set up in long format with multiple observations per person.) BMI changes over time - not fixed - so this method allows you to use all observations available.


Laura Wang, Dermatology

  • We are using a GVHD consortium data set to look at how skin GVHD disease progression may predict non-relapse mortality. We have the body surface percentage affected by erythema for followup visits at 6-months intervals, in addition to relapse date and death date. We would like to see how the rate of change correlates with non-relapse mortality.
  • Background: Bone marrow patients receive transplants which attack host cells. Skin is most commonly affected organ (erythema or sclerosis) and is best the clinical predictor of how well the patient will do. Type of skin disease and percentage of body affected
  • Study: Does rate of change in body surface area pct correlate with outcome? Outcome is overall survival or non-relapse mortality. There are 13 all-cause deaths in the current data: sample size becomes an issue, as you need 10-20 events per parameter in the model.
  • Suggestions: Model should include age, BSA% at visit 1 (initial erythema), and one of the change over time parameters (not both). Consider adding age as a non-linear term (age-squared, or restricted cubic spline) if you have enough patients in the model and can put an extra parameter into the model. Multiple univariate analysis are harder to interpret and not recommended, since we cannot tell how parameters affect outcome when accounting for one another. If including all incident cases, would need to add additional parameter to denote acute versus chronic, but extra cases would possibly allow you to add more parameters.


Alex Cheng, Biomedical Informatics

  • We are planning a prospective study to assess the relationship between treatment workload, capacity to manage care, and outcomes in patients undergoing treatment for breast cancer. We have put together a collection of surveys from PROMIS and other sources to give to patients over 5 months after the start of treatment. We need some help coming up with the proper analytical plan and sample size calculation for the study. A previous study that most closely resembles this one is this one However, we want to draw a more direct relationship between the imbalance of workload and capacity and outcomes.
  • Seeking VICTR voucher/help with study protocol
  • Survey data collected via RedCap for 1 medical center, survey is currently 96 Qs
  • Hypothesis: Imbalance of workload and capacity can results in worse outcomes in breast cancer. Patient workload (personal life + medical demands) versus patient capacity (resources available to the patient: finances, insurance, etc). Objectives: demonstrate the correlation of imbalance to health outcomes in patients undergoing breast cancer. n=104 (52 lost to FU)
  • Planning to perform MLR
  • Recommendations: likely need more cases, especially since half patients don't have complete data. 400 patients required to estimate correlation with margin of error of 0.1. Ideally reduce number of questions on survey to less than 30 - can give different questions to different people. Risk high non-response bias, since non-reponse is likely related to workload. Could collapse dimensions via factor analysis/variable clustering. Pre-specify the strategy of dimension reduction but not final summarization. Could follow-up with patients only once, randomising what time they are contacted, to increase independence and reduce number of dropouts.

Audrey Bowden, Biomedical Engineering (walk-in)

  • Hypothesis: clinic OCT can identify CIS (carcinoma in situ) against inflammation in bladder cancer. Training group has received a biopsy; ideally biopsy would be avoided due to comorbidities. Recommend to search for a graded histology (rather than binary yes/no) to train group to the highest signal.
  • First need to identify those included in the study and clear study outline. For sample size, base it off prevalence of parameter of most interest in population.


Benjin Facer, Epidemiology

  • I am using the National Cancer Database to compare outcomes between laparoscopic surgery and robotic-assisted laparoscopic surgery. I have run several comparison tests, which have resulted in various p-values and confidence intervals, but I would love some guidance on if I’ve used the right tests and am interpreting them correctly. Data is in R. Time frame of 2010-2014, with follow up through 2017.
  • Comparing robotic surgery versus laparoscopic surgery, for outcomes being measured are 5-year overall survival, conversion to open percentage, length of hospitalization. Only some have biopsy, so not everyone has a wait time between biopsy and surgery; explain to reader in manuscript that this is the case. Reasons given for type of surgery are not available. Robotic surgery available in 30% of hospitals and 70% of surgery; depends on availability of robot and surgeon experience level with use of robot. No randomized trials so far.
  • Need to ask preliminary question: what is the propensity for a patient to get robotic surgery? To denote randomness of getting robotic-assisted surgery. Need robot availability data, geographical location, busy-ness of robot, surgeon experience level, patient preference, etc. First paper: propensity model to use robotic procedure. Then in second paper, analysis to compare outcomes.
  • Is there a specialty that only does laparoscopic or only does robotic? Need to consider differences in patient characteristics, institutional characteristics, surgeon characteristics; case experience volume of individual surgeons or centers/institutions...
  • Models: Logistic regression for conversion to open (yes/no), including center-level characteristics, propensity score...; Cox model for length of stay in hospital. Will report the coefficient and confidence interval of the surgery type.


Sarah Osmundson, OB/GYN & Maternal/Fetal Medicine

  • 1) Want to compare patient-reported opioid use to use documented from track caps. 2) Have dates/time of opioid use after discharge for cesarean and want to graphically present data.
  • Outcome: pill unused (Pillsy cap pill tracking)
  • Want to compare patient report to Pillsy report. Also describe pattern of opiod use over time and interaction with ibruprophen. n=~176, ~ 100 with Pillsy. Online survey sent two weeks after discharge. Need to consider date of delivery (people d/c on different days post delivery). Pillsy data imported into redcap. Frank suggested event chart, plotting event over time. Time in days (or fraction) on x-axis. Could select five or so by algorithm to present in manuscript. Reccomend using delivery date not discharge date--could stratify by days in hospital. Possible time to event analysis, or time to milestone. Could do scatter/bubble plot of self report vs Pillsy.


Dylan Williamson (Walk In), Ped endocrinology

* Ashey Shumaker is PI. We need PI to provide maningful assistance. Question is related to z score and importing z score into database. Lots of problems with standardixation in the population.


Inga Saknite

  • Hematopoietic cell transplantation (HCT) is the only potentially curative option for an increasing number of patients with hematologic malignancies and other non-malignant conditions. 20,000 allogeneic HCTs are performed annually in the US. Graft-versus-host disease (GVHD) occurs when the transplanted immune system recognizes the host as foreign and mounts an immune response. Acute GVHD (aGVHD) develops in 30-60% of patients following HCT, is one of the leading causes of mortality in the immediate post-transplant period, and is associated with substantial morbidity and mortality. Both timing and accuracy of aGVHD diagnosis are important areas of unmet need in the first 100 days post-transplant. Although the diagnosis is relatively certain if multiple organ systems are involved (i.e., skin rash, diarrhea, and increased bilirubin), many of these correctly diagnosed patients die because it is difficult to halt the inflammatory cascade at this stage of clinical presentation. Treatment decisions are highly dependent on the diagnosis, and need to be made quickly. Early intervention is vital to reduce mortality, and identifying early signs of aGVHD before clinical presentation is an important unmet need. An imaging biomarker could lead to improved outcomes by supplementing clinical decision-making and reducing delays in treatment.
  • The pathogenesis of aGVHD involves the activation and expansion of donor leukocytes which mediate cytotoxicity against host cells. The inflammatory response causes increased expression of specialized endothelial proteins on vessel walls making leukocytes roll, adhere and eventually extravasate into the tissue at a high rate. The nature and kinetics of leukocyte migration are thus intimately connected to aGVHD pathophysiology. Other groups have described and characterized dynamic leukocyte motion by intravital microscopy in mice. Important parameters include the level of leukocyte rolling (number of leukocytes rolling per minute per vessel length), adhesion (leukocytes stationary >30 seconds), and the rolling leukocyte velocity. The level of leukocyte rolling and adhesion can be seven times higher in GVHD compared to control mice. Leukocyte-endothelial interaction has previously been observed by RCM in human skin, but has not been explored clinically. We will assess all three of these parameters as potential imaging biomarkers by testing their ability to discriminate presence from absence of aGVHD. Study ends when patient gets GVHD.
  • Aim: Test the feasibility of confocal imaging biomarkers in 30 patients to predict the development of aGVHD. We will track patients prospectively through multiple imaging sessions over the course of the first 30 days post-HCT. First, we will longitudinally image 15 patients over 30 days by using the Vivascope1500. We hypothesize that there will be a significant difference in the maximum number of rolling and adherent leukocytes between those who did and those who did not develop aGVHD within 60 days post-HCT. Second, we will image 15 more patients by using the high-speed, portable confocal microscope. We hypothesize that the high-speed, portable confocal microscope enables a more precise measurement of the quantitative parameters, and a reduced imaging time.
  • Question 1: What is the best approach to test the statistical significance of data of 2 groups (control vs. disease) when the data is acquired longitudinally (specific parameter changes over time after transplant)?
  • Question 2: We have preliminary data of a cross-sectional study (disease vs. control), 10 patients in each group, 2 parameters for each patient (number of adherent leukocytes, number of rolling leukocytes) at only 1 timepoint (NOT longitudinal data). For an R21 grant, we would like to discuss power analysis calculation.

  • Recommendations:
  • a) Investigate the correlation between adherent and rolling leukocytes. If there is some correlation, consider combing them in a model. Let the data speak for itself. Could be increased adherence and rolling prior to becoming GVHD. Three observations were excluded from graphs because they later developed GVHD, but they should be included in model. Use grade of GVHD (Booksberg scale). Goal is to predict (with a prospective longitudinal study) GVHD; this is hard to do with a dichotomous variable, would need a large sample size in order to do so. Determination of GVHD comes from multiple organ systems; but typically better to measure one system really well rather than dichotomising.
  • Think about this project as learning about trajectories; being able to classify by following trajectories or following trajectories of those who are already in either group. To estimate probability of GVHD without knowing prevalence, need at least 96 patients (just to estimate intercept of logistic reg model, without biomarkers). If you have an idea of prevelance, can estimate sample size with that known range and sample size will likely be smaller. Don't consider forced classification (GVHD or not) but rather use a tendency outcome. Typically requires minimum of 200 patients for only one biomarker.
  • b) If considering a proof of concept study, to see if something can distinguish the two groups, can search for a signal in the marker; allows for equal numbers in groups. Look at distribution of GVHD versus non. Nonparametric comparison of medians (Wilcoxon test) possible, however many observations still needed; power calculation based on Wilcoxon test required. (Large outlier in current data implies large possible variability.) Test as 0.025 level in current data. Pay attention to confidence intervals; if you want to CI to be half as wide, need 4x as many observations.
  • c) For longitudinal study, examine slope change. Longitudinal mixed model possible, however with pattern unknown hard to know how much data. Longitudinally, probably only able to describe data, rather than test it.


Garrett Booth, Pathology

  • QI project looking at US chargemaster costs for blood products. Help in statistical analysis for various blood product costs. Help in geographical mapping of cost data.
  • Background: Wants to be able to mentor others within pathology department about using biostats services. Every procedure carries a CPT code so that people can be billed. 1% of hospitals operating budget goes to path/blood products. No one knows true cost of blood.
  • Purpose of study: to identify true cost of blood that goes to patients and look for regional trends. What is the best way t olook at blood products? By type of cell? By procedure (some procedures require fractionating blood, which some insititutions charge for and others do not)? Can we identify differences in hospital costs? Would like to look at common procedures and look at how in line (or out of line) certain hospitals lie geographically and cost-wise. Goal to write comentary about limited biological supply and arbitrary billing structure. How much of cost can be attributed to geographical effects; how much of variation in cost is explained geographically? Geographic location captured by zip code in dataset. Goal: demonstrate difference, then speculate reasons why. 78 academic medical centers included in data.
  • Notes: hospitals in expensive cost of living areas may reasonably increase costs for indeterminable reasons. Will not be able to differentiate those reasons, so some hospitals may appear to go against regional economic trends. Useful to use relative charges (e.g. bed-days), rather than absolute charges? Rates for procedures among different insurance companies are not publicly available. Red cross (controlling 40% of blood supply) does not charge every hospital the same.
  • Recommendations: With the hospitals spread all over the country, so there's not much use in geographical mapping. Generally, geographical analysis can be performed using GIS, which will use zip codes and can bring in census bureau information. (Problem: catchment area for some hospitals is very wide, inter-state.) Could identify private/public health insurance as a proxy; tells you who is not paying out of pocket. Useful to gather population density by zip code, or by census tracts; using address/lat/long, map those to FIPS codes or shapes files (used by GIS) which have the characteristics to be used for geographical analysis. Cost data tends to be skewed, so nonparametric methods or log-transformation to normalise data is required. Storytelling using maps (thermometer plots) for comparing single products or grouping of products. Statistical model could include rural/urban variable (determined by popul density; accounting for other possible explanatory variables) and raw charges, to create raw model and map those against adjusted charges to determine what amount of variation is explained by measurable things and what is not explained. See how the amount of things not explained by variables in the model vary by region. (Rurality, number of hospitals/hospital beds per capita in catchment, etc available in census data.)
  • Next steps: come back to another clinic before applying for VICTR voucher to further develop research plan; talk to Health Policy department for information re: health economics (John Graves)


Rachel Koch, Surgery

  • I need help with coding of string variables from Redcap and then would like to confirm that I am using the correct test to compare groups given my data and perhaps also to discuss ways to find the most interesting results from all of the data. Mentor confirmed, may be late.
  • Project: Perceptions of underserved care in Kenya, by residents in program. Comparing residents who went through program before/after rotation was implemented.
  • Issues with likert scale responses: treating as linear and using mean, ties in data.
  • Recommendations: For analysis of survey data, give difference of means for unpaired data and margin of errors/confidence interval. In dataset, create 'long' data with one variable for likert score and one variable indicating in which group each participant is. Numerically code the likert-scaled variables (after ensuring ordering of string variables are correct using value labels) to use in t-tests. Use IF statement to select only those post-rotation to compare two Kijabe groups.

Madison Wright, Chemistry

  • This is part of a class assignment through Bruce Damon's Experimental Design for Biomedical Research Course. My project focuses on understanding protein-protein interactions as they pertain to protein folding. I'd like to address methods to evaluate data normalization of quantitative mass spectrometry based data sets.
  • Project: protein-protein interactions as it changes through disease-states. What are best methods to normalize the data? (Tuesday clinic may be more helpful.) Base protein is in all six conditions being compared. Intra-disease comparison of proteins (~1000) with base protein, and inter-disease comparison of each protein (difference between each protein and base) across disease. Thirteen (independent) runs.
  • Recommendations: Log-scale the data if copy numbers are low. Investigate correlation to determine the appropriate sample size. If 6 proteins from the six conditions in the same run, sample size is effectively 13. If no correlation, ss is 6x13. Specify compound symmetric correlation structure in protein between diseases (any pair of the 6 you measure is equally correlated) to estimate rho/correlation. In regression model, can choose to control for the base protein, could include raw number in the model but the starting value as a covariate. Multivariate analysis will include six dependent variables. Beta on log-term in model denotes fold change normalization.


Jenna Dombroski, Biomedical Engineering

  • Request: Maria and I are students in Dr. Damon’s PHAR 8328 Experimental Design course. My project is to test the efficacy of a vaccine I have developed to prevent 4T1 breast cancer in a Balb/c mouse model. Maria’s project is to synthesize a dual functionalized liposome which will target and kill circulating tumor cells in the bloodstream before they can form a distant metastasis. Our questions are related to pilot studies, sample size and avoiding bias.

  • Maria: colon cancer metastasis. Studying a protein which comes out of cancer cells when they move through blood to other parts of the body. Staining and cancer cell images; protein appears as spots (puncta) on the cell surface in imaging. Getting 10-15 images of the puncta in the cell line. Needing to analyze the puncta on image (define, number - variation of puncta across cells - and size, typical shape, distribution/location). Using ImageJ partical analysis function. Will eventually build an AI. Staining/imaging process is long. Cell sizes are approximately the same.
  • Advice: Could look at density of distribution of puncta across cells. When measuring multiple units which mimic/influence each other (where there is less variability), more cells do not necessarily contribute new information. Could look at nearest-neighbor distances to evaluate distribution/location. In presenting, state assumptions (e.g. that cells are the same size). If two measurements are highly correlated (variable clusters) don't need to compare across both measurements, only one. Will help to establish the dimensions that you need to deal with, in order to organize output of interest. Recommend to follow-up with animal research biostats clinic. Recommend displaying all raw data with current data, due to number of cell lines. Scatterplot: number of puncta by another characteristic, colored by cell line. Could look into research of characterizing data on a sphere (contact Tom Stewart for contacts), parallel coordinate plots.

  • Jenna: Testing efficacy of vaccine in mouse model. Has performed pilot study: 3 test/3 control. Initial results: reduced tumor size. Goal: reduced growth, increased survival. Primary endpoint is time until death, following all mice for 6 weeks maximum (time until established tumor size). Measuring tumor size with imaging, every 2 days. Batch effect of housing mice together is unknown; assumed no effect.
  • Advice: Longitudinal profile recording size of the tumor over time would give most information/more power, using an endpoint of tumor size. Make a decision about how long to follow the mice (e.g. at end of 6 weeks, end follow-up of all mice). For full study, the researcher taking tumor measurements will need to be blinded.


Brett Byram, Biomedical Engineering (VUSE)

  • We are interested in doing A/B testing of some images as a way to assess improvement, but we would like to have a brief chat about the experimental design and how to analyze the data before we go farther. Outcome: Grant/abstract *Two images, which one better, or are they similar? Question: Which is image is preferred by physician? *Design: Want to assess consistency, as well. 10 images, repeated. *Could assign 0.5 point to answer "C" (similar). *Could not do binary, could do a "slider" and capture how much a physician prefers the image. *If have small number of readers, they need to read more images. Then can checked intra rater reliability, and assume that these readers will be the same as the population of all readers. *Could also do three images, three sliders. Or instruct readers to assess the first, and then compare the others to the first one.


Lara Harvey, Gyn

  • A comparison of surgeon times and scores on 3 simulation trainer tasks before and after a training session in Haiti. Question regarding best statistical test to compare times and scores.
  • No funding support expected.
  • 7 surgeons testing 3 skill sets in laparoscopic surgery technique; want to evaluate time and OSATS score pre- and post-intervention. Recommended to use descriptive tables and figures to describe data. Inferential stats not recommended due to small sample size. Wilcoxon rank-sum may be used.

Alexander Hawkins

  • Overview: Robotic surgery, with articulated instruments and the ability to perform delicate dissection in the pelvis, has been thought to offer an advantage to traditional laparoscopy. The specific aim is to determine if there is a difference in the rate of negative margin status between patients undergoing laparoscopic versus robotic resection
  • Data: National Cancer Database
  • Design: Retrospective cohort of laparoscopic and robotic approach for patients undergoing resection for rectal cancer.
  • Endpoint: negative margin status
  • Funding: Will apply for VICTR biostatistics voucher
  • Recommendations:
    • Adjust for potential confounding due to surgeon choice using propensity score methods.
    • Seek biostatistics voucher
    • The hours of support required for this project are projected to fit within the standard voucher.


Reza Ehsanian, PM & R

  • Design: Cross sectional population based study.
  • Data set: Comprehensive pain reports categorically defined as head, spine, trunk, and limb pain; smoking history; demographics; medical history from a total of 2,307 subjects from the 2003-2004 National Health and Nutrition Examination Survey obtained from the Centers for Disease Control.
  • Objective: Examine the interrelationship between smoking and pain.
  • Have questions about the analysis conducted. Want double check our methods and potentially receive input on how to improve analysis.
  • No funding support expected, mentor to attend by phone.
  • Result of discussion: best course of action to obtain the data and start over, in order to appropriately defend the analysis. Since smoking is a key variable of interest, use pack-years, time since quitting, multi-level smoking status (e.g. never, former, current).

Yuri Kim, General surgery

  • Would like to conduct a retrospective review of comparing clinical outcomes in trauma icu patients who received palliative care intervention.
  • VICTR voucher, mentor confirmed.
  • Trauma subjects with palliative consult or no consult
  • Need sample size
  • Outcomes: utilization: LOS, cost '
  • Propensity vs. regression: see Frank Harrell's write up at
  • Frank: back up, consider what factors are important in real time.


Jake Hughey, Biomedical Informatics

  • I am using the SD to identify medications that are associated with false positive drug screen results (where the sample initially tests positive by immunoassay, but then negative by the gold standard mass spec). I would like to know if my approach, which is based on constructing 2-way contingency tables, is reasonable.
  • Background of study: Immunoassays designed to recognize specific drug or class of drug. Then confirmed based on a more specific assay (standard practice). Immunoassays can recognize other molecules/compounds than what they're designed for. Systematically going through SD to use lab test results along with medication information to determine which drugs associated with false positive screen.
  • Question of interest: What is the probability of having a false positive screen (of a particular sample having a positive screen result and a negative confirmation result)?
  • Output measurement: 0 (screen negative) or 1 (screen pos, conf neg). Retrospective review of what medications the patient had an order for in the previous 30 days (arbitrary amount of time, drug likely to be in urine by that time). Only prereq: patient had their very first visit at least 30 days prior to screen (urine sample), in order to know what medications they're on. Analysis excludes patients who had both positive screen and positive confirmation. There is a small percentage of patients who have negative screen and positive confirmation, but they don't fit well into the study framework. Two of the medication compounds are similar/overlapping, however the medications tested are fairly distinct. Each observation is an individual screen, so the same patient may have multiple observations. Confidence of capturing medication data in patients. (OTCs are not documented, PCP may not be at Vanderbilt, brand names/generic are grouped together into same variable. Testing 700 ingredients across screens. Looking at correlations in medication usage, calculating pairwise Pearson correlations between top ~20 ingredients.
  • Recommendations: Look at confidence intervals instead of p-values, as CIs will give information about magnitude. Candidates that need to be in the combination are the ones which are not independent of each other/those which co-occur a lot (based on raw counts): use a logistic model including all of these combinations and the second-order interaction of those which co-occur a lot. Need at least 200 events ("1" outcomes in the dataset) to stabilize the logistic model (at least 5 people - not measurements but actual people - must have had a false positive on that medication for that medication to be included.) Could extend the available data by stacking the data/combining data from all screens; correct later for faking the sample size.


Aaron Brill, Radiology and Epidemiology

  • Project: 35,000 patients treated between 1946 and 1968 for hyperthyroidism with different combinations of I-131, anti thyroid drugs and surgery. Mortality data updated thru 2015 on 90%. Therapy not randomized. Much Co morbidity and biased treatment allocation. Known small radiation risk. To avoid potential radiation risk anti thyroid drugs used preferentially. Need to look at how different outcomes correlate with therapy, including effects on longevity, a potentially positive effect. Data regarding I-131 risk has been analyzed in collaboration with NCI but has not included drug and surgical therapy and as the initial study PI I want to look at the data as a Phase 4 type study to look at unexpected correlations and need to find a statistical approach and a statistician interested and skilled in using the available tools needed for such an analysis. Data at NCI and their collaboration will be needed.
  • Hoping to have a more clear analysis plan by M Jan 14
  • Advised to call it an 'epidemiological cohort study' rather than Phase IV study.
  • With many comorbidities, database will need thousands of outcome events to use individual comorbidities. May need to use comorbidity index to approximate impact of comorbidities present. Will need to choose the appropriate comorbidity index for your project.
  • If dataset has baseline information collected prior to treatment allocation, then a propensity score could be included as a covariate in a regression model. What were the physicians thinking when they made the treatment allocation? Factors may include calendar time, etc.
  • Swedish paper excludes many patients in their cohort, which may cast doubt on methods. Comorbidities could be included directly as covariates.

Jae Jeong (JJ) Yang, Epidemiology

  • Project: I am working on a cohort study to examine the associations of baseline characteristics (i.e., lifestyle and dietary factors) with weight change during follow-up using a multivariate mixed effects model. I would like to have your comments on how to select adjustment variables for our mixed models.
  • 18000 patients in dataset with baseline time point. Outcome is continuous variable of weight. Exclude patients with severe disease at baseline. When a patient develops a severe disease, they are excluded, and when a patients reaches age 70, they are excluded from study. Data from Southern Comm Cohort Study. Follow-up data is collected at yearly intervals.
  • What are primary covariates of interest? Lifestyle, psychosocial factors, medical hx. With all covariates included in model, some are significant and some are not.
  • If goal is inference, recommended not to use a variable selection procedure and to include all variables. Automatic variable selection causes CIs to become too small and type I error rate is not protected. If goal is prediction, can use a variable selection method.
  • Due to size of cohort, the number of covariates included in the model are not a concern.
  • Analysis done by sex and race.
  • Outcome variable should be what you measure in the follow-up and baseline variables could be nonlinear. (For age and weight, could put variable + variable^2; or could put an interaction term in as a secondary analysis.)
  • One model: baseline covariates. Second model: baseline and follow-up covariates. (Test R^2 for change/effect of follow-up time points.) Third model: include interaction terms.
  • If lack of follow-up is due to baseline characteristics is related to issues other than baseline characteristics, need to state in limitations.


Shawniqua Williams Roberson, Neurology

  • Seeking VICTR biostatistics voucher
  • We conducted a 35-question survey among epilepsy patients in the outpatient clinic to explore racial and socioeconomic differences in attitudes toward epilepsy care. Hypothesis: African Americans express less trust in their providers and greater perception of dangers of surgery than other populations. Question: would like assistance in developing statistical analysis plan and statistician support for completing the analysis.
  • Prelim analysis done on 36 subjects. Would like to complete analysis and produce paper.
  • Survey pulled from literature (prev published in Canada). n=144 (123 able to be analyzed; 20 unable to complete, 1 aborted during interview) Survey delivered as an interview. Qs about epilepsy are categorical/binary. Qs about providers are Likert scale. Demographic Qs are categorical/ordinal. Data are in REDCap, exported into Excel.
  • Goals: validate survey, produce demographics, inferential analysis looking at relationships between race, attitudes towards providers and towards surgery.
  • Next steps: In StarBrite, go to Funding > Apply Here. At one point (under Resources part of application) it'll ask for the type of support you want, specify a biostatistics voucher. The VICTR voucher is flat-priced and will automatically populate the budget. In Documents, will need to put together a 5-page written application. (Tom will send a template for this 5-page document by email.) Correspond with Tom to agree on stats section, before submitting application.

Brenda Pun,

  • Seeking VICTR biostatistics voucher
  • As part of my DNP dissertation I worked on a survey to ICU interprofessionals about teamwork and healthywork environment. My dissertation focused on those data from one site as a pilot study. Since then I have worked with a national professional society to collect the same data from 6000+ ICU professionals nationally. I am planning to submit a VICTR resource request for the funding to support the statistical analyses of the national dataset.
  • Goal: implement critical care bundle. premise: teamwork matters. resurveyed staff 14 months after initial survey. AITCS and HWE scales given to all staff in critical care in 68 hospitals. collaborative is all anonymous; incorporated the dan-rosh (sp?) method to pair pre/post responses. (30% of post-collab responses possibly to be paired with pre)
  • Now: secondary analysis in this project. 1. descriptive at baseline. 2. what factors influence teamwork scores? 3. is there a difference before/after collaborative? 4. are there any predictors of this change pre/post?
  • Funding: funding secured through professional organization. Would need a contract (through the cost center): funding would go to you, the researcher, then would come to biostats as the analysis is done. Able to apply for VICTR voucher, if you like.
  • Deadline: aiming to have manuscript out by end of spring 2019.
  • Thomas Stewart to be in touch via email to follow-up.


Sophia Delpe, Urology

  • Seeking VICTR biostatistics voucher, mentor confirmed.
  • Our study is a cross sectional survey sent to women >18. We would like to look at the prevalence of fecal incontinence and the relationship between that and psychosocial disorders/social interaction.
  • Questionnaire on REDcap assessing toileting behavior. Approx 4789 patients.
  • More of a descriptive study, so should describe the distribution of responses in results (e.g. histograms). To assess bivariate relationships, recommended to present cross-tabulations for categorical and likert-scale questions. Could use regression models: tendency to stay home modeled by symptoms, etc. Next steps for future work would be to control for covariates (age).


Caroline Thomas, Pediatric Pulmonology

  • Seeking VICTR biostatistics voucher.
  • Retrospective chart review of pediatric patients with obstructive sleep apnea, who underwent tonsillectomy and adenoidectomy, and were then placed on positive airway pressure (PAP). We would like to determine whether there are predictors of adherence to PAP, specifically looked at: sex, race, insurance, weight, BMI, developmental status, presence of genetic disorder/autism/and/or psychiatric disorder, age of diagnosis of OSA, initial findings on sleep report, time to initiation of PAP post-surgery, other surgeries, presence of PAP titration study, presence of comorbid sleep disorders, follow up visits to sleep clinic, use of auto or fixed PAP settings, use of psychotropic medications, and data of nightly usage from PAP downloads.
  • Adherence outcome measured as hours in first 6 months of use; adherence is at least 4 hours per night. n=117, download data for 67. Other variables of interest: development/neurodevelopment (prior diagnosis) and binary verbal variable.
  • Statistical software recommendations: previous use of Stata so will continue to use. Missing data will likely be approached with multiple imputation.
  • Suggestions: Descriptive statistics by adherence. Covariates: baseline CPAP score, age, development, verbal, (possibly) weight, interaction between age and developmental status. For analysis, stick to 6-8 parameters in the analysis, due to sample size of 67.
  • Next steps: 1) Send Tom Stewart an email to get started with VICTR biostats voucher and work to get something together for abstract (due December).

Alexander Sherry, Radiation Oncology

  • Seeking VICTR voucher
  • Prospective trial of concurrent chemoradiation in adjuvant treatment of breast cancer. Our question regards a power calculation for our primary aim. Would be happy to provide more details (protocol) prior to meeting.
  • Feasibility study. Primary aim of grade 3/4 clinician-derived toxicity during treatment (binary endpoint) sample size calculation of 17, but VICTR studio questions.
  • Recommendations: Perform precision analysis to give estimate of yield of study regardless of how big a difference is there. With feasibility study, main objectives are to show that you can get patients enrolled (within reasonable time, resources, etc.) and that you can measure what you're trying to measure. To derive and validate another quantitative measure in the feasibility study could allow for more efficient full study. A "feasibility/measurement study".
  • Concerns: To not distinguish grade 3/4 toxicity, requires more samples. Could possibly consider ordinal regression, depending on proportions of 3/4. Ask what estimating and bump SS up by factor of 10, or be aware and transparent about what the current SS can show. Noise requires more samples. Typical SS is 384 for MOE of 0.1. Non-inferiority SS are even larger.


Alan Tate, ENT Clinical Instructor Faculty

  • Trying to export REDCap data with certain criteria and then categorize. Previously attended a REDCap clinic.

  • Study involves voice patients, about five years of data. Four groups, voice therapy alone, PT alone, and combo VT and PT. Observational study; patients selected group, essentially. Two questions: how were they different at baseline, and how were they different after therapy. Could look at differences in groups at baseline using bivariate approach. Then, perhaps multivariate approach to second question.

  • Possible biostat voucher. Email Tom Stewart for VICTR application.

Christopher Gray, Neuro/Stroke

  • Requests assistance with data interpretation for a review of current Kcentra protocol for intracranial hemorrhage.
  • Previous clinic visit on Thurs, Sep 13, 2018: Requested advice on how to present data meaningfully. Advised to 1) look at outcome (death at 30 days) in a logistic regression with the size of the bleed as the independent variable, and 2) look at severity of rankin at 30 days using proportional regression.
*Feedback. Don't use correlation for binary variables. Try to show change in Rankin with profile plot--current plot does not show change well. Trying to define question? Not clear--right now all subjects got Kcentra, weight based dosing. Hospital may switch to standard dosing, if weight based is not effective. Stick to outcome of probability of success of treatment--


David W. Bearl, Pediatric Cardiology

  • My proposed project is evaluation of liver studies (labs, MRI, elastography) pre- heart transplant for Fontan patients (all have liver disease pre, which is known) and then evaluating those patients post- heart transplant (that is not known).
  • n = 31 since started doing transplants for kids in 1987. Repeat evaluation at 6mo and 1yr post-tx.
  • Two steps: (1) feasibility: show you can actually collect the data for the larger study. Estimate pt-to-pt variability; rates/patterns of missing data; (2) larger study with other hospitals: proper sample size needed for this (powered based on feasibility study).
  • Best option for small population: ask 'based on where the patient started, where did they end up?' Can make use of gap between follow-up evaluations as a variable, if the gap varies by patient.
  • With half the patients, better to show descriptive statistics (graphs and tables).

Shawniqua Williams Roberson, Neurology

  • Purpose: Preparing preliminary data for an upcoming career development award submission. Several quantitative EEG metrics have been recorded on patients with ICU delirium of varying etiologies. Would like to use these preliminary data to build a model that uses the qEEG metrics to predict the etiology of delirium. Need guidance on: 1) how much data is needed to build this model 2) what statistical tools to use (multivariate logistic regression?)
  • Applying for Faculty Research Scholarship in next cycle (February). Hoping for guidance on how to analyse preliminary data with respect to an aim in the grant.
  • Aims of research: Evaluating quantitative frontal EEG to monitor for delirium continuously, producing numeric output. (1) Is it better than traditional EEG? (2) Can we distinguish different etiologies for delirium; Is there a dominant one, at which we can direct clinical decision-making? (3) Does qfEEG predict adverse outcomes? (Note: qfEEG is a subset of traditional EEG which doesn't require an EEG reader.)
  • Data: 25 patients, 89 assessments. (Measurements taken at least twice per day, over the course of up to two weeks. Summarised down to a single number within the window for each assessment.)
  • Suggested analyses: (1) random-intercept model (accounts for the fact that observations within a patient will be more highly correlated with each other): RASS ~ covariates + random intercept per pt. In scatterplots, put RASS score on y-axis since its the outcome. Depending on sample size, you could possibly allow for a non-linear association between variables. Could possibly focus on hypoactive patients only. Ordinal regression model with random-intercept, since RASS is ordinal and scale is small. (Not every statistical program will have ordinal regression with random-intercept, so may have to revert back to linear regression with random-intercept.) With 4 different predictors from the EEG and 89 assessment, should have enough to look at non-linear associations. (2) To differentiate etiologies, need patients with all types of etiologies and take the qfEEG. Regression is one method for creating that prediction tool. Once the tool is developed, collect more data and see how the tools perform making those predictions on the new data. Preliminary steps for etiologies: see how etiologies show up in graphical displays. (3) Depends on what you do during steps 1 & 2. Potentially its own separate predictive model with different features seen in the data. A lot of data will be needed for all models.


Joseph Wong, Biomedical Informatics

  • Purpose: Building upon a prior project–we have measured satisfaction, health literacy, and computer attitude regarding the patient portal prior the eStar EHR migration. We now want to measure these same factors with the new, eStar-based patient portal. From the original 6000 survey respondents, 3000 have volunteered to be contacted again.
  • Previous clinic visits on Th 8/16 & Th 9/06: Investigating determinants of patient satisfaction with an online patient portal (My Health at Vanderbilt). Had previously built univariate linear regression models for satisfaction score and had selected factors for a multiple linear regression model. Recommended to add histograms. Recommended to include only up to a quadratic term and a linear term in the model. Recommended to use square root transformation on the Health Result Function, rather than the logarithm, and to include both the square root and linear terms in the model.
  • Previously tested satisfaction usage before eStar update (using old pt portal) using ordered logistic regression. Next step is to test satisfaction with eStar. Needs to know what to measure and get thoughts for data collection.The same individuals agreed to be followed up with the eStar satisfaction. Satisfaction scale is 12-60.
  • To fix odds ratios in ordered logistic regression, need to transform (square root or cube root - cannot use log due to zeroes) count (click) variables and then use interquartile ranges, rather than raw values, as change in one click is negligible. Do transformation before implementing regression model. Easiest: do transformation, divide by iqr, use those values in the regression; interpretation made by change in IQR. (Demonstration of restricted cubic spline in Stata: can calculate odds ratios but beta values cannot be interpreted.) Can model all count variables this way. Test statement will allow to test the overall impact.
  • Compare satisfaction pre-EPIC to post_EPIC with Wilcoxon signed-rank or paired t-test. Not interested in testing computer literacy as it may not have changed in the previous six-months. 3000 individuals agreed to be recontacted. Best to keep the survey short; only plan to ask the 12 satisfaction questions. Keep in mind that people may respond differently to satisfaction questions if previously at end of longer survey and now shorter survey. Other test options: use same model already built, Bland-Altman plot, identify what may correlate with a decline in satisfaction. To calculate session times to include in the model. Could look at post- scores as a function of pre- scores, nonlinearly. Could compare domains of satisfaction score to see if the weighting is equal.


Brooklynn Bailey, MMC Dept of Family & Community Medicine

  • Clinic Follow-up from 8/20:
  • We are exploring the relationship among PTSD symptoms in our sample of young women exposed to interpersonal violence. 17 symptoms are assessed via clinical interview and are scored from 0-8. Prior to our first clinic visit, I had ran network analyses in R, with concerns pertaining to sample size. We have since ran hierarchical cluster analyses as recommended to us to compare to the network results. We are returning to get feedback on these results and recommendations for next steps.
  • Current state: cross-sectional data; n = 68; 17 PTSD symptoms assessed through interview (each scored 0-8): not likely to be any 1s (each scored for presence and severity); histograms have been created by cluster, as recommended from last time (zero-inflated data: should report on number of 0 responses but overlay the probability distribution only over results 1+); results of cluster analysis (ward's method) with bootstrapping (open to suggestions on this front); a second cluster analysis based on presence of symptoms alone. * Analysis performed in R - function cluster methods are ward D and euclidean. Bootstrap method used is unknown (code used not available during clinic).
  • Concerns: some variables may not be grouped because of low variation/smaller sample (C8/B2/C12) are these clustered together because of low variability? These are all rare symptoms; are they grouped together only because of this or are they actually correlated? What is stability of bootstrapping?
  • Previously there was a question about sample size, so wanted to view variability of responses in available data. Matrix of pairwise probabilities for how often symptoms correlate in % of bootstraps. Probabilities should be either close to 1 or close to 0, so you'll see what clusters often go together; if getting values in middle of range, there is more variability in the way variables are clustered.
  • Need to think about the cutoff for what determines a cluster. There are algorithms for determining this cutoff, but they are very computational/use cross-validation. This may be something to handle via email after looking at code. Also need to think about adjusting the number of clusters.
  • Q: How could we tie these back to original network analyses and validate? A: There is a very tight connection between the clustering and the network analyses because both based off correlation matrices. Once you've ID'd that you have stable clusters, you can do network analyses within each cluster to generate partial correlations.
  • Q: Is the last symptom to separate out more central than other symptoms? (Interested in identifying centrality.) A: With longitudinal data centrality of the symptoms makes more sense, so not useful in cross-sectional data. (If unable to reject null, unable to detect clusters in stable way. Clusters perform better with more data, therefore sample size may be issue.)
  • Next steps: Brooklynn to send code to Dr. Stewart for review. Stability question to be answered after viewing code.


Christine Rukasin, Medicine/Allergy, Pulmonary and Critical Care

  • I am doing a survey based study evaluating anxiety and drug allergy testing. This is a series of surveys with repetition of questions at different point in time. I would like assistance in strategies to best analyze the results, visualization/diagrams of results and suggested sample size. * Expected Outcome: Protocol with no expected funding support, Abstract, Other. Possible VICTR voucher? Still time before analysis is needed. *Graphical display of data. Could sum questions for a total score. 100 is a reasonable number of subjects. Could also plot mean score by number of tests/measures per subject to assess learning effects. Compute correlation with subject characteristics and total score. Tom will send email with VICTR application.

Satya (Nanu) Das, Medicine/Oncology

  • We are performing a retrospective analysis assessing whether gastrointestinal cancer patients (at Vanderbilt) who experience immune-related adverse events while on immunotherapy experience improved outcomes (PFS,OS,duration of response) compared to patients who do not experience these events. I would like to briefly touch on my data collection and the statistical methodology for my future analysis.
  • Expected Outcome: Abstract, Other
  • All subjects on immune therapy are eligible. How to disentangle treatment for event and treatment? We don't know how long they need to be on therapy. Could do "landmark" analysis, analyze one outcome (AE), then the next outcome. This is all subjects--with smaller sample, focus on high resolution variables.

WALK IN: Parisa Samimi, Uro gynecology

  • Possible VICTR, prospective study looking at correlation between patient satisfaction and am labs (no lab vs. routine labs). Do not know sample size needed. Do not know baseline satisfaction, or any baseline data. Question needs refinement--need to specify question and definitions. Could also search current literature for baseline satisfaction level--to get baseline data.


Mallory Hacker, Neurology

  • Study Objective: To improve the identification and referral of patients who may have spasticity to a physician who is an expert in the diagnosis and treatment of spasticity through the development of a bedside physical exam referral tool for primary care physicians and nurse practitioners. * Hypothesis: A simple limited bedside physical examination guide enhances the ability of primary care providers to correctly and reliably identify residents in a long-term care facility who may have spasticity and appropriately refer them to a specialist for spasticity evaluation. * Question: Are the sensitivity and negative predictive values the most appropriate to report for this study? * Expected Outcome: Other * Present as a 2x2 table (most will want to see), report PPv and NPV. Calculate SP SN, but not as primary number. Could do a figure to show proportion correctly diagnosed. Should we use Kappa? No--not the point. Also check instructions for authors.

Sean Collon, VUSM Global Health

  • Teleophthalmology screening in Nepal–comparing in person decision making of ophthalmic technicians with limited screening resources to decision making of ophthalmologits reviewing photographs of the same patients. For each patient, technician and MD record a diagnosis for each eye and a plan for each patient based on their respective information (in person exam with limited equipment vs. viewing photos remotely), diagnoses and plans grouped into broad categories, then agreement compared to determine utility of device in the screening camp setting. * Expected Outcome: VICTR Biostatistics voucher *Could separate by anterior and posterior, would make sense in this context. Could also do each diagnosis separately, then order in order of agreement. Agreement on treatment plan not useful when diagnosis did not agree, limit to when diagnosis did not agree. For agreement, could do a 2x2 table (MD/Tech). Calculate agreement. Two eyes from each patient--correlated measures. Could treat as independent. Can compute confidence intervals for all measures.
*Could put voucher in under local mentor name--although students may be eligible.


Brooklynn Bailey, Meharry Vanderbilt Alliance

  • I recently was introduced to the network approach to psychopathology at a conference this year. I would like to explore the network structure of DSM-IV PTSD symptoms in my sample of young adult women who have recently experienced interpersonal violence. I have taught myself how to conduct network and related analyses in R; however, I have some questions related to my small sample size and the adequacy of my findings given this limitation. In general, I could use guidance on methods for analyzing this symptom data to better understand the presentation of posttraumatic psychopathology in this population. *Possible VICTR voucher-contact Tom if interested. * Research question: how are PTSD symptoms related to each other in this sample of young women? *Sample size ~70 *Using Lasso right now * Consider using histograms to examine structure of symptom data. Could do simple variable clustering with bootstrapping with replacement, less complex than current approach.

Rohini Chakravarthy, Meharry Vanderbilt Alliance

  • We have surveyed a cohort of 3000 patients using an IOM survey on social determinants of health. We are interested in seeing which are most predictive of outcomes (as measured by A1C at time of study and potentially its progression). I think multilevel modeling may be useful but am not sure how to proceed and whether this makes sense for a VICTR voucher application. Data collection is complete.
*First step is further refining question; are we looking at med adherence, incidence, or AIC? *Multiple regression may be right approach, even in the presence of colinearity. Could do voucher or continue to come to clinic for more assistance.


Jessica Heft, Urology/Urogynecology

  • We will be conducting a survey of young women and assessing their physical activity and how that relates to pelvic floor dysfunction. We will be using several standardized questionnaires and need assistance with methodology/patient recruitment expectations/statistical planning. Project is in the design phase.
  • VICTR Voucher to cover biostat support-can set up database alone. May ask for VICTR support for gift cards.
  • Propose email based survey examining relationship between athleticism and stress incontinence.
  • Concern is over the representatives of the respondents.
  • Recommend using slider bar for questions when possible.


Zeb White, Hearing & Speech Sciences

  • A new, experimental 40-question parent-report measure was developed by our lab in order to better understand parent-child interaction in stuttering. This instrument was administered to 68 parents of children who do and do not stutter. We are attempting to understand the differences between the two groups (parents of children who stutter vs parents of children who do not stutter) and identify if the instrument correlates with other parent-report measures regarding stuttering severity and consequences. * We would like guidance in selecting appropriate statistical tests to answer relevant research questions. * Data collection is complete. * Range of stuttering severity, not "true" group. Kids could range from ~4% of words to ~15%. Really is a range (0% to ---). About 30 in each "group". BUT, could include previously excluded kids which would increase sample size considerably. * Wish to reduce items on survey, perhaps group questions? * Questions developed from advice given to parents and from literature on parent intervention. This survey administered at time 0, prior to intervention/therapy. * Could look at correlation; parent response by RYCS; does the degree of stuttering correlate with the RYCS? Could use Goodman gamble. * Small sample size to do 40 analyses--use caution with multiple tests like Wilcoxen. * May consider dropping "never" and "always", extreme responses. * 40 x 40 correlation matrix could show what questions are highly correlated, and drop highly correlated. * Redundancy analysis could work * Cronbachs alpha on questions that should measure the same thing * Could force questions into groups based in clinical (e.g. timing)


Margaret Adgent, General Pediatrics

I am updating an analysis from an observational cohort study regarding maternal prenatal vitamin use and childhood asthma. Pregnant women were enrolled and interviewed; they were recontacted 4+ years later to answer questions about their children’s health. There is substantial loss to follow up (70%), and I am interested in applying inverse probability weights to address possible selection bias due to loss to follow up.
~1900 met inclusion for the secondary analysis, ~500 responded and had exposure. Goal is to compare folic acid use before pregnancy to those who started after. Should summarize the differences between groups (those with follow up and without)--see how different they are. There is and fairly even split between groups. Use 1/prob Wt. table to check values, check for large values Contact Jill Shell re: collaboration in peds. Possible Chris Slaughter.


Laurie Samuels, Biostatistics

  • The project uses Medicare claims data to look at regional rates of variation in a particular surgical procedure, and I would love to get feedback from more senior biostatisticians. Looking at regional variation in colon resection. Have three years of data. Several issues, one is difficulty in identifying denominator. Dartmouth health atlas could be useful for methods.


Brenda Pun, Pulmonary

  • As part of my DNP dissertation I worked on a survey to ICU interprofessionals about teamwork and healthywork environment. My dissertation focused on those data from one site as a pilot study. Since then I have worked with a national professional society to collect the same data from 6000+ ICU professionals nationally. I am planning to submit a VICTR resource request for the funding to support the statistical analyses of the national dataset.

  • Stage of project (select one): Data collection completed

  • Data collection method (select one): Survey

  • Data management system (select one): Redcap

  • Expected outcome (check all that apply): VICTR Biostatistics voucher

  • Investigator experience (select one): Independent investigator


Natalie Covington, Hearing & Speech Sciences

  • We are planning a study in which we would like to sub-classify patients with traumatic brain injury based on their memory “profiles” (patterns of impaired and intact memory performance across a battery of tasks); we would like to discuss possible methods for classifying patients into subtypes (e.g. latent profile analysis; k-means clustering; etc).

  • Stage of project (select one): Design

  • Data collection method (select one): Other

  • Data management system (select one): Spreadsheet

  • Expected outcome (check all that apply): Protocol with no expected funding support

  • Investigator experience (select one): Graduate/Medical Student


Wendi Mason, Medicine / Pulmonary

  • To compare a new practice model (prospective) employing telehealth strategies of telemonitoring and telesupport to previous year’s model of standard practice (retrospective chart review) to determine effect on hospitalization rate, illnesses and other complications, compliance, and rate of decline in patients with Idiopathic Pulmonary Fibrosis.

  • Stage of project (select one): Design

  • Data collection method (select one): Case report form/data form

  • Data management system (select one): REDCap

  • Expected outcome (check all that apply): VICTR Biostatistics voucher

  • Investigator experience (select one): Independent investigator


Jessica Heft, ObGyn/Urogyn

  • Retrospective cohort comparing two surgical approaches (open vs. laparoscopic). Will be looking at perioperative complications and outcomes.
  • Stage of project (select one): Design
  • Data collection method (select one): Data are exported in electronic format
  • Data management system (select one): REDCap
  • Expected outcome (check all that apply): VICTR Biostatistics voucher
  • Investigator experience (select one): Resident or fellow
Discussion & Action Items:
  • Perfect confounding between surgeon and surgical technique.
  • Jessica will coordinate with Thomas Stewart to develop a statistical analysis plan for submission of an application for VICTR voucher.


Yolanda McDonald, Human and Organizational Development

  • The editor-in-chief of the American Journal of Public Health asked for us to test for interaction indicating that there is heterogeneity across the 4 size-specific ORs . I found some information on Research Gate. However, I would still like to discuss the test or test(s) option. The manuscript is Minor Revision status.

  • Stage of project (select one): Data collection complete

  • Data collection method (select one): Other

  • Data management system (select one): Other

  • Expected outcome (check all that apply): Other

  • Investigator experience (select one): Independent investigator


James Andry, Neurology - Sleep

  • Please provide a short description of your project and the questions you’d like to address: The primary goal of this study is to evaluate whether the features measured by an aggregated set of consumer-grade activity monitors can predict a given patient’s successful treatment with CPAP. Our study design also supports the secondary goal of validating the sleep parameters measured by these devices in aggregate. Would like to discuss statistical methods for measuring correlation between sleep parameters from consumer-grade devices (test device) and polysomnography (gold-standard).

  • Stage of project (select one): Design complete but no enrollment/data collection

  • Data collection method (select one): Data are exported in electronic format

  • Data management system (select one): Spreadsheet (e.g. Excel)

  • Expected outcome (check all that apply): Protocol with no expected funding support, VICTR Biostatistics voucher

  • Investigator experience (select one): Independent investigator


Alexander Langerman, Otolaryngology

  • Please provide a short description of your project and the questions you’d like to address: Using qualitative research, we’ve identified subgroups of patients who have differing opinions on how they trust their physicians. I’d like to develop a quantitative diagnostic of these perceptions.

  • Stage of project (select one): Design

  • Data collection method (select one): Survey

  • Data management system (select one): REDCap

  • Expected outcome (check all that apply): Other

  • Investigator experience (select one): Independent investigator


Ellen Kelly

  • Please provide a short description of your project and the questions you’d like to address: We have developed an instrument to assess parents’ perceptions of their communicative interactions with their children. We need assistance with evaluating the instrument and analyzing the data we have collected to date.

  • Stage of project (select one): Data collection underway

  • Data collection method (select one): Case report form/data form

  • Data management system (select one): REDCap

  • Expected outcome (check all that apply): Protocol with no expected funding support, Other

  • Investigator experience (select one): Independent investigator

Briana Furch, Infectious Disease

  • Please provide a short description of your project and the questions you'd like to address: I'm not sure which type of analysis I should do in order to compare 4 different disease states and their associated biomarkers (variables) at different time points. I also want to look at these disease states to asses normal variance.

  • Eligible for departmental collaboration plan, if in place?: no

  • Stage of project (select one): Design

  • Data collection method (select one): Other

  • Data management system (select one): REDCap

  • Expected outcome (check all that apply): Protocol with no expected funding support, Grant

  • Investigator experience (select one): Independent investigator

  • Name of Mentor: John Koethe


Paul Slocum with William Stuart Reynolds (mentor), OB/GYN

  • Please provide a short description of your project and the questions you'd like to address:
Assessing pain in women with synthetic pelvic mesh and outcomes after treatment.

Would like to come to biostats clinic to obtain VICTR research voucher. We have a prospectively collected case series of patients with pelvic pain who underwent mesh removal.

  • Eligible for departmental collaboration plan, if in place?: no

  • Stage of project (select one): Data collection completed

  • Data collection method (select one): Data are exported in electronic format

  • Data management system (select one): REDCap

  • Expected outcome (check all that apply): VICTR Biostatistics voucher

  • Investigator experience (select one): Resident or fellow



Ray Blind, faculty, Department of Medicine, with two undergraduate students

There is a lot of data correlating IV drug use with hepatitis, and hepatitis with liver cancer, but no studies have correlated IV drug use with liver cancer, to our knowledge. We used the synthetic derivative to attempt to correlate IV drug use with liver cancer and need help deciding which stats tests to apply to the data.

  • Best approach would be to follow a cohort of IV drug users to see whether they develop liver cancer; next-best would be a case-control study comparing the odds of being an IV drug user among people with liver cancer compared to people in a reasonable comparison (control) group. The hard part is deciding what that control group should be.
  • Here is an introductory explanation of case-control studies:
  • To visualize 2x2 data (for example, IV drug use by cancer) graphically, you can make a jittered scatterplot. This gives the same information as a table, but it can be helpful to see the information presented in more than one way.



Dupree Hatch, Pediatrics

  • Please provide a short description of your project and the questions you’d like to address:

I have two projects that I would like to discuss the design of a statistical analysis (if there is time):

We have a large national database that contains data on ~20% of all very low birth weight infants. We would like to a) describe the use of mechanical ventilation in these infants (# of days, etc.), b) quantify the inter-center variation in the # of ventilator days/infants and c) define the contributions of specific practice variables (ventilator modalities, sedation regimens) to the observed variation in ventilator days/patient. I would like to discuss the statistical analysis to quantify the variation and to test the practice factors to attempt to determine what, if any of them are driving variation.

The second study I would like to discuss if time allows concerns alarms from mechanical ventilators. We have built an internal database of ~30000 hours of ventilator alarms. I would like to describe some of the factors that are associated with high alarm burden (patient size, ventilator mode, time of day, etc.) in a future effort to intervene on those factors that are modifiable. I would like to discuss how to handle the clustering at the patient level since we have hundreds, sometimes thousands, of alarms within a single patient and how to adjust for that when I look at the different patient and practice factors.

  • Stage of project (select one): Design

  • Data collection method (select one): Data are exported in electronic format

  • Data management system (select one): Spreadsheet (e.g. Excel)

  • Expected outcome (check all that apply): Protocol with no expected funding support, Grant, Other

  • Investigator experience (select one): Independent investigator

  • Notes from clinic:
    • Chris Slaughter has a collaboration plan with Pediatrics but is currently busy; Dr. Hatch came to clinic for preliminary discussion
    • For the first project:
      • 200--300 centers; 10--200 very-low-birthweight births per center per year; 6--7 years of data. Temporal trends are likely but seasonal trends are not.
      • Interested in quantifying the resource utilization (number of ventilator days)
      • Even the descriptive statistics are challenging for this project, because some of the babies die, and ventilator use is a measure of both how sick the baby is and of usual practice at that particular center. It's possible that the best approach will consist of a mixture model that incorporates both time to death and ventilator days while alive.
      • Some babies are transferred from their original NICU. Rather than excluding these babies from the study cohort, we recommend including them in the cohort, but censoring them at the time of transfer, to minimize bias.
    • For the second project:
      • The dataset contains 40k ventilation hours for 400--500 babies. About 15% of the patients get switched from one mode to another
      • It will definitely be important to include patient characteristics in the model for this project; it may be less important to do patient-level clustering, depending on the data structure and the overall goal of the analysis.


Nitya Venkat, Undergraduate Student, Vanderbilt Brain Institute.

In our study, we programmed a MATLAB script and Arduino micro-controller to deliver visual and tactile stimuli to subjects. We then collect responses (numerical: 0 -100) from subjects as well as questionnaire responses on a Likert scale (1-6). We are hoping to address how to deal with the issue of normality in the data as it relates to parametric tests. We are also hoping to ask about which post-hoc tests to do, what means to correlate and generally how to make the most of our data. We also have questions about how to correlate our Likert responses to our measurement data that is numerical but not on an interval.

W. Stuart Reynolds, postdoctoral fellow, Urology.

My project is concerned with base-line clinical characteristics of women with and without overactive bladder, including general demographic and clinical data, along with condition-specific data and results of quantitative sensory testing, with which to phenotype participants. I am interested in phenotyping, specifically using data-driven statistical methods, such as clustering, and would like advice regarding these and other novel techniques, including machine learning, that may be applicable to my data. I am planning to submit for a VICTr voucher for biostatistical support.


Lou Posey, medical student

The acute phase response is the body’s biological response to combat bleeding, infection, hypoxia, and tissue dysfunction following an injury. This system is tightly regulated such that a post-injury response that is either too small or too robust can result in deleterious patient outcomes. This trend has long been observed in clinical practice, yet the validation of clinical markers of the acute phase response (also known as acute phase reactants) in correlation with poor outcomes is underreported. Using the synthetic derivative database, we aim to correlate vascular complications (namely venous thromboses) with elevation in acute phase markers such as CRP. Moreover, we will record associated platelet levels surrounding the vascular complications to depict a consumptive coagulopathy.

Currently, there are no quantitative markers to predict the risk of a DVT; as such, we hope to show the divergence of elevated CRP and platelet trough as a novel predictor of thrombosis. This could change VTE prophylaxis guidelines in both the pediatric and adult populations.

Lauren Marlar, PUBH student

Sample size calculation and and test selection for class assignment.

Problem: Calculate the sample size required for a randomized controlled trial comparing two treatment groups and a control group. The primary end point is a 5% weight loss by the last session. Assume that only 5% of the participants in the control group will lose at least 5%. Assume that 20% of the people who start the program will drop out.

Note: We were instructed to go to the Clinic if we needed assistance. (Note from Laurie Samuels: Dan Byrne confirmed that he suggested that students in this class attend clinic for help with power calculations.)


Claire, Lo. Medical student.

I have data and preliminary analyses from a repeated measures longitudinal study assessing the impact of variable exercise intensity and volume on inflammatory markers (hs-CRP, IL-6, epinephrine). I am trying to create generalized linear models for the data and I'm not sure where to start (or if GLMs are the most appropriate model for this data set).


Sarah Diehl, Hearing and speech sciences (doctoral student)

The speech perceptual characteristics of people with dysarthria due to chorea can vary tremendously (Darley, Aronson, & Brown, 1969a). The current study aims to identify distinct clusters of speech perceptual characteristics within a group of 51 speakers with dysarthria resulting from Huntington’s disease (HD). All speakers will be within a mild speech severity range.

Raters (4 graduate students complete, 6 to be recruited) completed a speech perceptual characteristics checklist for each person with HD. The speech perceptual characteristics checklist contains 38 items separated into 7 separate dimensions as follows: pitch characteristics (1-4), loudness (1-9), vocal quality (10-18), respiration (19-21), prosody (22-31), articulation (32-36), and general impression dimension (37-38). The general impression dimension also includes an estimated percent intelligibility (without formal calculation) and graduate students’ proposed dysarthria type. Each checklist item is rated individually on the ordinal scale from 1 (normal) to 7 (very severe).

The following research questions will be addressed in this study:

  • What are the speech perceptual characteristics consistent with diagnosis of HD and how do they compare to previous literature on hyperkinetic dysarthria?
  • Are there distinct clusters of speech perceptual characteristics within speakers with mild dysarthria due to HD?
  • If distinct clusters of speech characteristics exist within speakers with HD, do the individuals who belong to the same cluster also share other disease- or treatment-related features (i.e. type of medications, number of CAG repeats, and the length of disease duration)?

Our questions for the meeting are primarily focused on the cluster analysis, however, we may bring additional questions at that time. We plan to present preliminary results at a conference in mid November. We will bring full data for the first 4 raters.

  • Do not do statistical tests comparing groups on the 38 items that went into the cluster analysis. It would be reasonable to do tests on things like medication use, etc., that did not go into the cluster analysis.
  • To show group differences visually, consider plotting the first two principal components using colors for the different clusters, and/or making a parallel coordinates plot
  • With so few patients relative to the number of items, the clusters are likely to be unstable, although if the plots show large separation between groups, this may be less of a concern
  • Consider trying a few different clustering methods to see whether they all suggest the same four clusters
  • Consider sparse principal components analysis, and either cluster on some or all of the principal components, or use the PCA results to help you decide which variables to cluster on
  • For a manuscript, present intra- and inter-rater reliability

Supisara Tintara, Nephrology (medical student)

We are studying the tissue sodium levels in peritoneal dialysis patients compared to controls without kidney disease. My question is determining whether the sodium levels in dialysis patient is different from the sodium levels in the controls. Also, are sodium levels different among age, race, or gender in controls and dialysis patients.

  • Because some of the data has been published already, focus on two comparisons, using Wilcoxon rank-sum tests: PD vs. HD and PD vs. control
  • For comparisons involving race and gender, use descriptive plots rather than statistical tests (because the group sizes are very small)


Petrice Cogswell, Radiology (resident)

Analysis of survey response data from survey polling radiology programs directors of attitudes towards MD-PhD vs non-PhD residents. I verified that the distribution of respondents (program size and PhD residents) is representative of the polled group and would like to continue with assistance on the statistical analysis.


Petrice Cogswell, Radiology (resident)

Survey on radiology program directors toward MD PhD residents and resident research. The responses were likert scale, how do you view PhD residents vs non PhD score -2, -1, 0, 1, 2 representing much worse, worse, similar, better, much better in multiple areas. Question: is there a statistical test to evaluate this type of data?


  • First step: see how comparable the responders are to the non-responders (or the whole set of programs) in terms of # residents, # MD/PhD residents, NIH funding amount
  • From there we can talk about statistical testing. We will probably want to use a finite-population correction since the whole population = 63 programs
  • Regardless of comparability, descriptive statistics (10 out of 23... etc.) will still be interesting to report

Yolanda McDonald, Human & Organizational Development/Peabody College (Faculty)

Project Title: An environmental justice review of drinking water quality in the United States, 2011-2015

Abstract: Despite the need for potable water for human life and EPA regulation of U.S. public water systems, there has not been a comprehensive study to quantify disparities in residential drinking water. This research systematically reviews results of the National Primary Drinking Water Regulations (2011-2015) by community water systems at the county-level. This study utilizes an environmental justice framework to (1) elucidate if legally enforceable drinking water quality standards differ based on community race/ethnicity, socioeconomic status, and rural-urban classification and (2) determine if communities with predominantly underrepresented groups are disproportionately burdened with repeat violations of drinking water violations.

Data Sources and Variables:

Dependent Variable: Drinking water violations for arsenic, atrazine, chlorine, coliform (Pre -TCR), coliform (TCR), combined uranium, di(2-ethylhexyl) adipate, di(2-ethylhexyl) phthalate, nitrates, nitrate-nitrite, lead and copper rule, radium, TTHM, TCE, haloacetic Acids, and Trichlorethane were downloaded from the Safe Drinking Water Information System (SDWIS) federal reporting services for the years 2011-2015 (N = 58,018). Of the violations, there were N = 30,981 repeat violations. Violations and repeat violations were operationalized as dichotomous variables (0 = no violation, 1 = violation).

Explanatory Variables: Race/ethnicity and socioeconomic status variables were obtained from the U.S. Census, American Community Survey, 5-year estimate (2011-2015) and were operationalized as continuous variables measured as proportions. The rural-urban classification is based on the USDA’s Rural Utilities Service (USDA RU) definition of rural. Rural-urban classification was operationalized as dichotomous variables (0 = urban, 1 = rural).

Data structure: The database structure format is one violation per row, Public Water System ID (PWSID) is the unique identifier. A PWSID may appear more than once in the database. For example, a PWSID could have multiple violations during the study year. The column data points are violations, race/ethnicity, SES, and rural-urban classification.

Proposed Data Analysis Strategy: The unit of analysis is county-level. Descriptive statistics were run to characterize the data. Correlation matrix measured the magnitude and direction of association between water violations and explanatory variables. To determine the relationship between water violations and the explanatory variables univariable and multivariable logistic regression analyses were used. The Variance Inflation (VIF) diagnostic was used to detect multicollinearity in the multivariable and interactions analyses. To detect confounding, all explanatory variables unadjusted odds ratio were compared to adjusted odds ratio to determine if there was a change of ≥ or ≤ 10% in the odds ratio (Szklo and Nieto 2014). And, multivariable logistic regression was used to adjust for confounding (Pourhoseingholi, Baghestani and Vahedi 2012). The Pearson goodness-of-fit statistic was used to compare the observed values to the expected. Covariates that had a likelihood ratio P value of <0.050 (two-tailed) and an odds ratio that did not cross 1.00 with a 95% confidence interval were considered to be statistically significant in the univariable and multivariable analyses.


Do we need to adjust for water systems, i.e. counties vary in the number of community water systems that service the area? If so, which of these options are recommended? a. Do we need to adjust the variance estimators of the estimated coefficients to account for the variance within the county, i.e. robust standard errors using clusters? b. Weight counties by population served by the community water systems? c. Stratify by community water system size (i.e. number of people served): Small Level 1 ≤ 3,300; Small Level II 3,301 ≤10,000; Medium 10,001 ≤ 50,000; and Large ≥ 50,001.

Do you recommend that we use Pearson goodness-of-fit statistic to compare the observed values to the expected?

Do you recommend post-hoc analysis for logistic regression? If yes, are there different post-hoc test for interaction terms?


  • We are concerned because we don't know the number of times each system was tested. If it's not possible to get this information, one possibility might be to simulate data to try to get a sense of the possible scope of the impact of frequency-of-testing
  • The overall project seems like a good fit for a VICTR voucher or short-term biostatistics support (its scope is too large for clinic). To inquire about short-term biostats support, email Yu Shyr, Chair. Another possibility might be working with a student (email Jeffrey Blume, director of graduate studies).
  • Will need to keep in mind: some systems get swallowed up into other systems.
  • Longitudinal data analysis won't be feasible without the complete testing data (we would need the non-violations in addition to the violations).
  • Next level (after other issues resolved): geospatial correlation (tricky, though, because of the upstream/downstream issue)


Kelly Schuering, Internal Medicine/Vanderbilt Familiar Faces (medical student), with Ed Vasilevskis (mentor; Department of Medicine, Division of General Internal Medicine and Public Health)

This study is looking at the prevalence of housing instability, risk factors for instability, and utilization of community resources among patients working with the Vanderbilt Familiar Faces program. Our research questions are as follows: Primary: Among patients with high health care utilization working with the Vanderbilt Familiar Faces staff, what is the prevalence of housing instability, potential future housing instability, and secure housing and what factors predict this? Secondary: What community resources are people using to help address housing instability and how would individuals describe their relationship with those resources? What predicts whether patients are connected to resources to assist with finding housing?

Data will be collected through a self-administered redcap survey on an ipad while patients are in the hospital.

Our analysis plan is as below:

Housing stability (ordinal):
  • Pearson’s chi-squared or Fisher’s exact test, depending on n in each category
  • Ordinal regression (vs. multinomial?)
We plan to include the following variables in the regression based on literature review and experiences with similar populations: consistent income (binary), employment (binary), current substance abuse (binary), legal history (binary), and current/recent intimate partner violence (binary)

Resource usage (binary):
  • Pearson’s chi-squared or Fisher’s exact test, depending on n in each category
  • Logistical regression
Based primarily on our experiences as there is not literature in this area, we plan to include having an outside case manager/social worker (binary), having a regular monthly income (binary), history of drug use (binary), and current housing stability status (categorical) in this logistical regression.

Since our multivariable analysis will not be able to account for every potential confounder, we will also conduct a sensitivity analysis to determine how big of an effect an additional cofounder would have to be to change the observed relationship.

Finally, we will also do a subanalysis of the pre-identified VFF patients compared with those who were assigned to the VFF team due to risk and bed space. This binary variable could also be included in the multivariable analyses.

I was hoping to get feedback on the above analysis plan and input on how many variables can realistically be included in the regressions if the estimated sample size is 200. I am also hoping to get an estimate for how much time we would need to purchase from biostats in order to get the above analysis completed.


For the binary outcome, the best-case scenario would involve 100 patients per outcome group, in which case it would be reasonable to adjust for 5 covariates in the regression model. Pre-specifying the covariates without looking at the data would preserve the Type I error rate, but with an exploratory analysis like this, that might not be your highest priority. If you are planning to present the analysis as exploratory and don't need to prespecify the model, a good starting place would be to look at plots and descriptive statistics for all variables by outcome group, and then to look at a scatterplot/correlation matrix and also to make a variable-clustering plot to get a sense of whether some variables can be used to "represent" others.

An ordinal logistic regression would be appropriate for the ordinal outcome, but depending on the sizes in the groups, you may need to collapse two of the outcome groups.

Possibilities for longer-term help: VICTR voucher and/or contacting Dr. Yu Shyr, Chair, to see whether short-term help is available.


Christian Okitondo, Psychiatry (Staff)

Topic: Increased tendency for proximal proprioceptive errors in limb bisection for individuals with autism spectrum disorder is not mitigated by too use.

Previous studies involving tool use tasks have shown that typically developing (TD) individuals commit distal errors in limb bisection after using tools, presumably due to perceptual extension of the peri-hand space. Given that individuals with ASD are less susceptible to visual override of veridical proprioceptive information in other proprioceptive paradigms, we hypothesized that individuals with ASD would not demonstrate these distal errors after tool use.

Questions I would to address: How to incorporate repeated measure on my ANOVA? For each subject, I have a pre training means and post training means. How to explain the repeated measure ANOVA to the world with no statistical background?

Ricky Shinall, Surgery (Assistant Professor)

I have a dataset consisting of about 350 responses to a quality of life instrument that has not been previously validated. I would like to get an estimate on the biostatistical effort needed to analyze the data for consistency and validity in order to obtain a VICTR voucher.


Devika Nair, Nephrology (Postdoc)

Attending this clinic is part of a requirement for my Biostatistics I class that I am attending for my MSCI, but I do have a question related to one of my projects.

I'm interested in exploring the coping behaviors of African American patients with advanced, non-dialysis dependent CKD. Based on what is available in the literature (which is limited), minority patients in general use religious coping to deal with the stresses of chronic illness. African American patients in particular seem to use denial/avoidant coping mechanisms. I believe that these coping mechanisms could in part explain why many of these patients disengage and disappear when the need for dialysis is mentioned. I believe that these behaviors are more related to cultural differences, rather than socioeconomic status or educational level.

If I am trying to illustrate a causal mechanism for why AA patients with adv CKD disappear when dialysis is mentioned (independent of their SES/educational status), would the best study design be to compare AA patients of both low and high SES, or would it be to compare AA patients with low SES with patients of other races with low SES?

  • You are welcome to come back to clinic, but as a member of the Nephrology division you are also welcome to work directly with Thomas Stewart
  • Recruit patients across a range of SES's; will probably want to limit to patients who are either AA or white, due to likely low numbers in other groups

Baldeep Pabla, GI (Fellow)

Also attending clinic as part of a requirement for Biostatistics I class for MSCI; particular project involves looking at a predefined set of SNPs in patients with and without GI cancer or metaplasia. Current literature suggests that environmental factors may play a greater role than genetics in the development of these conditions.

  • You are welcome to come back to clinic, but as a member of the Gastroenterology division you may be able to work directly with Chris Slaughter
  • Identifying appropriate controls for this study will be tricky


Shaina Willen, Clinical Fellow (Pediatric Hematology/Oncology)

I am preparing a VICTR proposal to study the impact of biomarkers of lung injury on complications in children and adults with sickle cell disease. I would like some assistance with my statistical analysis plan and how to determine power and sample size calculations.

  • for enrolled patients, look at previous 3 years then follow forward 2 years
  • 250 children & 300 adults seen at clinic
  • plasma & DNA samples
  • outcome is pain and acute chest syndrome
  • 3 genotypes and plasma biomarkers
  • believe 2,2 genotype will have increased pain and chest syndrome (1,1 (26%) 1,2 (55%) 2,2 (19%))
  • look at incident rate at 1 yr and 2 yr; poisson model; need to know what difference would be expected between the groups
  • sample size - graph of incidence rate that can be detected vs sample size needed
  • Simplest approach: find confidence interval formula for a Poisson rate; assume lowest true rate and solve for n such that multiplicative margin of error is 1.5 with 0.95 confidence
    • Simplest confidence interval is lambda +- 1.96 * sqrt(lambda / n); once you have an upper limit on lambda can solve for n to give acceptable margin of error for lambda
    • Would be better to get the multiplicative margin of error for the ratio of two Poisson rates (to simplify we may assume the sample size in each group is the lowest of the three genotype group sizes)
    • See

Alice Hoyt, faculty (Medicine/Allergy)

The aims of this R21 are to determine the preparedness and knowledge of K-12 schools on the topics of asthma and food allergy, then to pilot an asthma telemedicine program.


Ricky Shinall (Surgery)

Palliative care consultation has been shown to reduce utilization in end of life care, but this hasn’t been rigorously studied in trauma patients. I have access to Vanderbilt’s trauma registry which can be cross referenced against the palliative care registry to identify patients with palliative care consultation. I’d like to discuss the procedures for creating a propensity matched comparison of patients with and without palliative care consultation to compare resource utilization between the two groups. I’d also like to get a sense of the complexity of this analysis and the amount of effort from a biostatistician it would take to complete it.

Propensity score model resource:

Katie Tippey (Anesthesiology)

We did a card sorting projects where each participant sorted 4 decks of index cards. We recorded the time it took them to sort and notes based on asking them to think aloud while they sorted the cards. We created annotated files of photos of their final sorting arrangement and have created a raw data set based on these photos. We think we may want to use factor analysis on this raw data set but are unsure both if this is the appropriate analysis and, if so, how to perform this analysis.

Resources: section 16.1


Nicolas Forget (Emergency Department)

  • Perception of collaboration between doctors and nurses in Guyana
  • Pre- and post-team-building exercise, then 4-6w later
  • 27 participants with 2 dropouts by the end; 15 nurses, 10 doctors at the end
  • Issue of using means vs. proportions for Likert scales; want to look at disagreements of perception before and after training
  • Nurses used more spread of answers than doctors
  • 2 demographic variables, 15 Likert questions; need to combine into a single global scale for graphical individual profiles and for stat analysis
  • Can do a formal analysis of variability of responses within subject, e.g. compute the SD over 15 questions within subject and see if nurses have more variation than doctors
  • Main analysis on mean
    • Form within-person difference from baseline (paired data)
    • Do 2-sample (unpaired) t-test comparing these differences - nurses vs doctors
  • Be sure to graph all measurements (on summary score)
  • Pre-post design often provides an upper limit to an intervention effect


Maureen Saint Georges Chaumet (fellow)

Project description: I am starting a project that compares the cosmetic outcomes of 3 different laceration closure methods in kids: sutures, tape and glue. I will also be looking at several secondary outcomes.

Overall, study design seems reasonable. Recommended that parents rate the cosmetic outcome of the laceration in addition to 3 reference pictures. If parent's responses can be captured via an online Redcap survey, consider the possibility of a sliding scale response.

Recommend 90 hours of biostat work. (Does not involve writing more than one manuscript)

Samuel Younger (Nurse Practitioner)

Interested in determining sample size and best statistical approach. HLM-SEM vs Path analysis?

Research Abstract

Many organizations are looking to their staff to creatively engage in improving the safety of patients. Further, within the Magnet health care environment, transformational leadership is the theory that has been promoted as core to the achievement of patient outcomes, thus is the core focus of this study. The purpose of this research is to examine the role that leaders play in bringing together elements of a safety culture and a climate of innovation that support and enable staff to engage creatively in improving the quality and safety of patient care. There is little empirical evidence in the nursing literature related to patient safety in an innovative climate, and none could be found that study the leadership behaviors of nursing managers that are conducive to an innovation climate and impact on patient safety outcomes in a Magnet designated, Academic Medical Center. Therefore, this study seeks to fill that gap in knowledge and expand the leadership and innovation literature to include patient safety within a Magnet work environment.

This research uses a multi-level, cross-sectional, descriptive correlational design aimed at examining the relationship between nurse manager transformational leadership and front line nurse rated patient safety score, and to further investigate how, if any, does communication and feedback about error and the innovation climate influence the relationship. The independent variables in this study are transformational and transactional leadership. The dependent variable is front line nurse rated patient safety score. The innovation climate is proposed to be a mediating variable. Feedback and communication is proposed to be a moderator variable between transformational leadership and patient safety score. The variables will be measured through an online survey based the three validated and reliable survey instruments (54 questions): the MLQ-5x short (MLQ-5x), the Team Climate Inventory-short (TCI), and Feedback and Communication About Error and Patient Safety Grade (subscales of the AHRQ Hospital Survey on Patient Safety Culture) which are all appropriate for collecting data about the perceptions of front line nurses.

If findings confirm these relationships, then in order to impact outcomes, nursing managers may need to be adept at navigating and promoting the complex nature of innovation through communication and establishing an innovation climate. In this context, leadership facilitates communication and an understanding of the innovation climate, which supports creative solutions to patient outcomes and improved quality, in this case, patient safety. On a practical level, this study will contribute to a greater understanding of how to prepare future nursing leaders for the challenges of a changing healthcare landscape through an understanding of what behaviors are necessary to generate innovative and safe care delivery models.

H1a: There is a significant, positive relationship between nurse managers’ transformational leadership as measured by the Multifactor Leadership Questionnaire (MLQ-5X) and nurses’ perception of patient safety as measured by patient safety grade (AHRQ HSOPSC).

H1b: There is a significant, positive relationship between nurse managers’ transactional leadership as measured by the Multifactor Leadership Questionnaire (MLQ-5X) and nurses’ perception of patient safety as measured by patient safety grade (AHRQ HSOPSC).

H1c: There is a significant relationship between nurse managers transactional leadership as measured by the Multifactor Leadership Questionnaire (MLQ-5X) and nurses’ perception of patient safety as measured by patient safety grade (AHRQ HSOPSC), but to a lesser degree than transformational leadership. Included per our discussion on transformational leadership predicting quality above and beyond that of transactional leadership.

H2a: There is a significant relationship between nurse managers’ transformational leadership as measured by the Multifactor Leadership Questionnaire (MLQ-5X) and innovation climate as measured by the Team Climate Inventory (TCI-short).

H2b: There is a significant negative relationship between nurse manager’s transactional leadership as measured by the Multifactor Leadership Questionnaire (MLQ-5X) and innovation climate as measured by the Team Climate Inventory (TCI-short).

H3: The relationship between nurse manager transformational leadership as measured by the Multifactor Leadership Questionnaire (MLQ-5X) and nurses’ perception of patient safety as measured by patient safety grade (AHRQ HSOPSC), will be mediated by innovation climate as measured by the Team Climate Inventory (TCI-short).

H4: The relationship between transformational leadership and patient safety grade will be moderated by feedback and communication about error. In terms of this relationship, transformational leadership will have a stronger, positive relationship with patient safety scores when feedback and communication about error is high.


Ryan Skeens (fellow)

This is a patient activation measure survey conducted on parents/caregivers of NICU patients. Survey will be conducted at NICU enrollment, NICU discharge, and 30 day after discharge. The hypothesis is that patient activation measure will decrease at NICU discharge but increase over time (30 day after discharge). In addition, characters such as social economic status that links to high patient activation measure will be identified.

The measure has been validated and used by mentor team. This is a fellowship project, and Ryan will apply an internal grant for the 6-9 months project. Further, CTSA support will be explored.

  • Sample size is fixed based on fellowship time. Power and sample size should be calculated accordingly.
  • Keep the measure in the continuous form (0-100) instead of dichonimization.
  • Consider to have CTSA statistician's early involvement at the design stage. Given this involves design, grant writing, data collection, data analysis, and manuscript preparation, a 90 hour work maybe needed.
  • As prediction is involved (identify characters that are related to high measures), model validation should be considered.



Danxia Yu, Epidemiology (faculty)

We will examine the associations of diet quality scores (assessed at baseline) with body weight change (from baseline to following visits) in a prospective cohort study. Generalized estimating equation model has been used in other studies, which we are not familiar with. We need statistical inputs on this model and the power estimation. We also would like to find a statistician whom we may work with on this project. Thank you.

  • If dropout is not random, either GLS with a serial correlation structure or a linear mixed-effects model would be more appropriate than GEE.
  • Do not collapse the diet variables into quintiles; leave them as continuous variables
  • For the power calculation, it may be possible to ask for conditional approval to have access to a subset of the data to get estimates of the quantities needed for a power calculation.
  • You can do a simplified power calculation with just one wave of data, and argue that the power will be higher when there are more data points per person.
  • Possibly useful R packages: longpower (thank you for bringing this to our attention!), pwr (in particular, the pwr.f2.test function).
  • Simulation could also be a useful approach, but it would also require some background information about the standard deviations of the variables

Joshua Cohn, Urologic Surgery (clinical fellow)

I have two questionnaire-based databases on overactive bladder that I have merged. I would like to use this data to develop a model that predicts bother based on symptoms and comorbidities and prioritizes necessary treatments. I am not sure if cluster analysis is the best way to do this.



Paul Yoder, Special Education (faculty)

I'd like evaluation of area under the curve (AUC) as a way to quantify the magnitude of the between treatment-group-difference and its confidence interval for RCT with repeated measures of the dependent variable. A reference for an example is Gallop, R. J., Dimidjian, S., Atkins, D. C., & Muggeo, V. (2011). Quantifying treatment effects when flexibly modeling individual change in a nonlinear mixed effects model. J Data Sci, 9, 221-241.

  • Email Hakmook Kang to talk about the possibility of working through the KC biostatistics core to get an estimate of how many children and timepoints you would need to do the flexible-breakpoint approach discussed in the article
  • We also discussed an approach using restricted cubic splines. It's possible that this approach would let you use fewer subjects; it may be useful even though you are expecting a linear relationship

Bryan Hill, OB/GYN (fellow)

This is a follow up from recommendations from 5/15/2017 regarding a logistic regression model of post operative complications as the output variable and clinical and demographic variables as the independent variables. The recommendations, in summary were:

1) Treating the outcome as an ordinal, rather than binary, variable if there are enough people in the additional groups

2) Look at the cross-tabulation between physician and sling type to see whether it is feasible to include both

3) Leave the continuous variables as is (do not categorize them). May want to consider log-transforming age.

4) Try variable clustering to see which variables may be collinear/redundant

5) Consider combining less important (less interesting) variables into a score

Goal for the session: to discuss results of the model.



No clinic---Memorial Day



Bryan Hill, Fellow, Gynecology

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. The primary aim of the study is to determine sensitivity and specificity of the administrative method compared to the manual reporting method. The secondary aim is to determine which risk factors are associated with having a complication.

We think that creating a logistic regression model would help address our secondary aim. Our plan is the following: setting the output as "complication present (1)" and using the variables: asa class, age, body-mass index, setting (outpatient or inpatient), sling type, attending, if a concomitant procedure was done, anesthesia time, operation time, smoking history, diabetes, and prior surgery.

Question #1: We need guidance on how many variables we can include in our model. Some have high numbers, and some are quite low.

#2 Some variables may influence each other. For example, sling type is heavily dependent on attending (they like to chose a particular brand or type). How do we adjust our model for that?

#3 It is known that older patients are more likely to experience complications. How do we determine if age is independently associated with "complication presence" versus just being a confounder influencing other variables?

Files we plan to append: data dictionary, STATA file, table of variables with total numbers of responses.

  • In deciding which categories to collapse, look at the sample overall (not by complication status)
  • To increase power, consider treating the outcome as an ordinal, rather than binary, variable if there are enough people in the additional groups
  • Look at the cross-tabulation between physician and sling type to see whether it is feasible to include both
  • Leave the continuous variables as is (do not categorize them). May want to consider log-transforming age.
  • Try variable clustering to see which variables may be collinear/redundant
  • Consider combining less important (less interesting) variables into a score
  • For binary logistic regression, we generally want to have 10--20 people in the smaller outcome group for every degree of freedom (continuous variable or single category) in the model
  • If you apply for VICTR funding, we recommend the larger time amount if you are interested in a publication or presentation. In your application, you can cite these notes as evidence that you have been to a biostatistics clinic.

Mike Temple, Biomedical Informatics, faculty

I am comparing the results of 2 surveys and need help calculating p-values and odds ratios to determine significance between the 2 surveys. I am using R

  • Get more information about the survey design (especially number of people surveyed) so that you can compare the response rates in 2012 and 2016. If they are not close to each other, it will be harder to justify comparing the results of the two surveys
  • If possible, get info about demographic makeup of the people surveyed in 2012 and 2016 from the organization's records. If, for example, the mean age of respondents is very different from the known mean age of the people surveyed, you will know that in at least that one aspect, the respondents are not representative of the people surveyed.
  • Chi-squared tests should be fine if the categories are exhaustive (but this is secondary to the nonresponse issue)
  • If possible, get more info about the outcomes and model specifications used for the regressions in Table 3.


Chirayu Patel, resident physician, radiation oncology

The project is VEEP-C - Visually Enhanced Education for Prostate Cancer, a randomized, controlled trial to assess the impact of a visual presentation on prostate cancer treatment decision-regret, anxiety, satisfaction, and patient-reported symptoms, in the radiation oncology department. The expected accrual for patients was 112 patients based on 120 prostate cancer patient consultations seen within a 6-month timeframe. Unfortunately, due to a drop in consultations, only ~30 patients have been accrued, and only 1 patient has completed external beam radiation therapy over a 6 month timeframe (other have undergone brachytherapy, surgery, active surveillance, or are still deciding).

1. The sample size is based on an instrument which only 1 patient has completed. As originally written, the study is not feasible. Determination of new outcome and sample size?

2. Role for interim analysis on secondary outcomes?

3. Thoughts on closing the trial due to poor accrual?


Cara Singer, PhD Student, Speech and Hearing

  • This project investigates speech-language imbalances in children. We are interested in the best way to measure imbalances using five standardized tests. Simple range scatter and standard deviation have been discussed. We are also interested in the best way to analyze whether increased synchrony between the five tests is associated with a decrease in stuttering frequency based on two years of development.

Hatun Zengin-Bolatkale, Faculty, Hearing and Speech

The purpose of the present study was to longitudinally assess sympathetic arousal (i.e., physiological correlate of emotional reactivity) of preschool-age children with persisting stuttering (CWPS), those who recover from stuttering (CWRS), and their normally fluent peers (CWNS) during a stressful picture-naming task. The apriori research questions/ hypotheses are as following:

The first question addressed whether change in SCL in response to stress at initial testing - close to the onset of stuttering - is associated with stuttering chronicity (i.e., persistence vs. recovery). We hypothesized that children whose stuttering persists, compared to those who recover and those who do not stutter, would exhibit increased skin conductance reactivity to a stressful picture naming task at their initial testing (i.e., prior to stuttering resolution for children who recover).

The second question addressed whether change in SCL in response to stress - approximately 18 months after their first testing – is associated with stuttering chronicity (persistent vs. recovered patterns). We hypothesized that children whose stuttering persists, compared to those who recovered and those who do not stutter, would exhibit increased skin conductance reactivity to a stressful picture naming task at 18 months-post-initial testing (i.e., after stuttering resolution for children who recover).

The third question addressed whether changes in SCL in response to stress are associated with changes in stuttering frequency. We hypothesized that for children who persist, compared to children who recover and children who do not stutter, increased skin conductance reactivity would be associated with increases in stuttering frequency.

We would like help from the clinic with the analyses of the hypotheses above, especially for #3.





Sarah Diehl, Hearing and Speech Sciences , PhD student

* Questions for the clinic:

1. After removing the ratings that have a mean score of 2 or below, there will be ratings that will highly correlate. Should we first do something like a multi-dimensional scaling approach to identify dimensions and then a cluster analysis to see how these dimensions cluster? Or do we throw all ratings (potentially 38 if none receive a mean score of 2 or below – realistically perhaps something like 20 to 25) into a cluster analysis.

2. If we expect at least 2 or 3 clusters, what is a reasonable sample size given the number of items we have on the rating scale?

3. What do we need to put into a proposal that is going to use cluster analysis? What kind of information is critical?

4. Is there another approach that would work better than cluster analysis?

Gurjeet Birdee, Health Services Research, Faculty.

  • The objective of this study was to measure the energy expenditure (oxygen consumption O2/kg/min) of adults practicing common yoga movements. For each individual, participants were asked to do movements in a standing position, lying position, and seated position (body orientation). In addition, each movement was done with different variations serially. In addition, participants were asked to walk at low and moderate intensities to compare energy expenditure of a comparative aerobic exercise to yoga.

The main questions we would like addressed:

What is the best approach to measure if there was significant variation between individuals for mean energy expenditure by body orientation?

What is the best approach to measure if there was significant variation between individuals for each movement?

When considering if variation exists above, should we take into account resting energy expenditure for each individual?


Cara Singer, Hearing and Speech Sciences, PhD student

  • This project investigates differences in skin conductance levels in children who stutter and are persisting, children who stuttered and recovered, and children who do not stutter. All children were followed 3-4 times across a two year period. At each visit, skin conductance levels were measured during a neutral video and speaking task, a positive emotion-inducing video and speaking task, and a negative emotion-inducing video and speaking task. We would like to discuss the best statistical models for our hypotheses.

  • Note that at each timepoint, there are 7 skin conductance measures (a "baseline" and 6 other measures)

  • Recommendations:
    • Keep all possible timepoints from all possible subjects. Do not exclude subjects based on their trajectories or baseline characteristics
    • Use continuous versions of the stuttering outcomes if possible; at a minimum, collapse the outcomes into 5 ordinal categories
    • Use a longitudinal mixed-effects model. Each subject will contribute 1, 2, or 3 rows depending on how many of the timepoints they have. You can model severity as a function of time-1 severity, age, sex, the seven time-1 conductance measures (or a reduction thereof; try a redundancy analysis first), time in days, and squared time in days, with random effects for subject (and possibly time and squared time). We recommend a continuous-time correlation structure, but this might be tricky with the mixed-effects model; generalized least squares might work better.
    • If we can get a clear, simple plan and the analysis is not a multi-step analysis and the dataset is clean (and tall and thin, with the relevant time-1 variables and non-identifying subject ID on each row), we may be able to conduct the analysis during a clinic.
    • Starting next month, we will be able to take on longer short-term projects for a charge.
    • The Kennedy Center statistics core may also be able to do this. If you come back to a clinic, please remind us to invite Hakmook.


Kristy Broman, Surgery Resident

Method to compare standardized incidence ratios using SEER data


Katie McGinnis (MPH candidate)

(followup from last two weeks)

  • For each overall question category, try a scatterplot of a) the means and b) the standard deviations for each item, with staff values on the x-axis and parent values on the y-axis (or vice-versa). Label each point with the question number or a short phrase to identify it
  • Do variable clustering within the staff items and the parent items, to see which items tend to be answered similarly by the same person (hcavar in stata)
  • Rather than doing several univariate analyses comparing the relationship between the demographic items and each survey item, do a single regression analysis for each survey item, with all the demographic items included in the model at once. Collapse the categorical items into 2 or at most 3 categories, and just assign numeric values (e.g. 1--5) to the levels in the binned continuous items like distance and treat those as continuous variables (so they will have just one term in the model). Actually, though, drop distance altogether and just use travel time. The overall F-statistic from the regression will tell you whether anything in the model matters. The best approach would be a proportional odds model, but ordinary regression will be next best.
  • It's ok to take the means of means (across items in a particular category) and talk about those, but there aren't enough data points to warrant a statistical test.


Katie McGinnis (MPH candidate)

(followup from last week)

  • Instead of doing t-tests, do wilcoxon rank-sum test (only 5 response options)
  • Rather than overlaying the parent and staff histograms, show the parent mean as a dot on the staff histograms
  • Do the "dot-histograms" by hospital because the hospitals are so different, even if tests comparing hospitals are not significant
  • Don't put too much weight on the p-values; this is exploratory research with relatively small sample sizes
  • For the two similar staff questions, run a correlation on the responses to help justify using only one of the questions. Use a Spearman rank correlation.
  • We don't think it would make sense to take the mean of the responses for the parent "how often" questions
  • For any set of questions, it could be interesting to order the means to see which questions had the highest or lowest means, but it wouldn't make sense to do a statistical test comparing the means of the different items.


Antje Mefferd, Hearing and Speech Sciences

I’m an assistant professor in the Hearing and Speech Science department and I’m currently preparing a manuscript. I would like to have someone take a look at the analysis that I completed to make sure they are correct. I’m a bit unsure about some things (assign fixed and random effects, reporting of degrees of freedom). I have my data in excel spreadsheets and can share it ahead of time.

The topic is how the tongue and the jaw change in their range of motion during various speech tasks (speaking typical, loud, slow, clear). These speech tasks are used in speech therapy to help people with brain diseases (Parkinson’s disease) to be better understood. In this data set I look at this in just one group of speakers (healthy speakers).

Participants complete 5 repetitions for each task (5 reps x 4 tasks = 20 data points from each participant). There are 11 females and 10 males in this study (sex has a significant main effect due to anatomical differences between males and females, but it is typically not statistically controlled for in our field in repeated measures). There are three measures – tongue movement, jaw movement , and the acoustics. For all three I need to analyze task effects in separate analyses. I also need to look at how changes in tongue movements predict changes in acoustics and how well changes in jaw movements predict change sin acoustics using data of typical to loud speech, typical to clear speech, typical to slow speech -- this time regressions within females and within males.

In the meeting I would like to make sure that I ran these analyses correctly and also would like to verify that I used to correct degrees of freedom in my write-up.

Recommendations: 1. For primary analysis, either ANOVA using each subject's mean or mixed-effects model with fixed effect for task and random effects for subject would be fine. 2. For secondary analysis, it would be best to use the same approach (either one mean data point per person per task, or a mixed-effects model). If doing mixed effects model for secondary analysis, be careful with the interpretation of R-squared.

Katie McGinnis (MPH candidate)

I have questions about my MPH Thesis project, specifically related to the best options for comparing some of my variables and running a few other statistical tests

Practicum in Kenya; originally a needs assessment, not designed for research. 16-page staff surveys (n= 94) & parent surveys (n= 69) from 2 children's hospitals, plus demographic data. Hoping to compare parent responses to staff responses in some way. Challenges: 1. parents are responding about 1 child but staff are responding about all children, and 2. for some items, the response scales for parents and staff are slightly or very different. She is comfortable treating the response options as numeric (taking the mean would be meaningful to her). The thesis does not have to contain a formal statistical analysis.

Recommendation for next steps: For survey items where the response scales are the same, continue the exploratory data analysis by plotting histograms for the staff responses, and then marking the mean of the parent responses on the x-axis.


Frances Anderson, MPH Global Health

I am an MPH Global Health track student and I need some assistance with ANOVA analysis on my thesis project. My project is an evaluation of Minnesota's TB screening of refugees and immigrants across four counties in the state. The data I am looking at for ANOVA includes mean days to initiation (TB testing) and mean days to disposition. There are some outliers in the data that I need to consider dropping. I seek advisement in this, completing the test, and if ANOVA is not appropriate for this dataset finding a new test.


Joshua Cockroft, MD student

We are looking to design and validate a new psychometric scale that measures a patient/client's trust in new providers. Though psychometric scales currently exist that measure trust in healthcare systems, trust in existing personal providers, and measures of global trust, there is currently no scale described in the literature that specifically measures trust in new providers. The hope is that such a scale would be of use in many underserved populations, particularly those populations with histories of either substance use disorder or severe mental illness, who are not regularly active participants within the healthcare system. We would hope to be able to use such a survey to measure the effect of this specific type of trust on outcomes such as healthcare service utilization. Like other healthcare trust-related scales, this scale would likely be a Likert-scale with questions that would span multiple domains of trust (i.e. competence, dependability). As there is no current gold standard for this type of measurement, advice on important considerations for internal validation would be greatly appreciated. We may consider the validation of this scale in multiple sub-populations if able. Conceptualization of this scale will be derived from the literature and our own qualitative research.


Angela Maxwell-Horn, MD, Assistant Professor of Developmental Pediatrics, Monroe Carell Jr. Children’s Hospital at Vanderbilt

I am a pediatrician wanting to do a study about the effectiveness of a medication to treat ADHD symptoms in children with autism. I would like to come to a biostats clinic to help me figure out what type of analysis that I should do and how many subjects I need to effectively power my study. I have attached a copy of my study proposal.

  • Recommend a randomized cross-over study design with double blinding if possible
  • Select a side-effect measurement tool
  • Clearly state inclusion/exclusion criteria

Heather Limper, Center for Clinical Quality and Implementation Research

"I would like to get some help with execution of times series analysis using STATA (ideally)."


Katie McGinnis, MPH Candidate, Global Health

Perform surveys in three children hospitals on parents and staff. 69 respondents from parents and 97 from staff. Parents survey: demographics about parents and children, how the experiences in hospital impact parents and children, patient satisfaction Staff survey: demographics, education, child's hospitalization needs

Research questions: what do you think caused the child's illness? The language barrier in receiving proper care? The correlation between child's experience in hospital and staff's education and experience.

Survey matrices are similar in parents' survey and in staff's survey (a dozen of likert-scale questions). Want to check the correspondence between parents' responses and staff's. First check if parents agree with each other. Code the answer to each question as 1,2,3,4,5. Summarize the score of each question across all the patients. Small SD is an indication of better agreement between parents. Second check the consensus of staff. Third, to evaluate the staff's characteristics, compare staff's responses to parents' consensus; to evaluate the parents' characteristics, compare parents' responses to staff's consensus. Take the difference between staff's response and parents' consensus as outcome, fit a regression model on providers' characteristics.

Could generate a summary score over multiple questions in one category (Rockwood's index).


Samantha Gustafson, Hearing and Speech Sciences

VICTR application for dissertation research. EEG measures for speech sound processing in quiet and in noise. Looking for age effects. How does the effect of noise change with age? Proposed analysis based on linear regression. Expects one EEG measure to be more sensitive than the other. Second question is to look for mediator with EEG response and how well they do behaviorally depending on age. Particularly tricky how to size a study for an exploratory mediation analysis. Have replaced repeated measures ANOVA with a linear model. Each EEG task takes 10 minutes. Two listening conditions, same task. Quiet vs noise order is randomized. Half of participants hear "da" and the other half receive "ga" (randomized). Model: EEG = intercept + age effect + noise/quiet + age x noise/quiet interaction. Can use generalized least squares (correlation structure irrelevant except don't assume the correlation is zero, since only 2 times per subject) or repeated measures ANOVA if very careful to use the correction for correlation (if can handle interaction between group (noise/quiet) and age). But GLS is ideal. Need to check normality assumption of residuals.

Power of a test of interaction is much lower than a test of main effect (difference in slopes vs. slope not being flat). Data not available for making initial guess of sample size required to achieve a given precision or power. Only thought is related to a minimum possible sample size - the size needed to estimate a difference in mean EEG for an adult with very good precision. The SD of the noise-quiet difference is used here. Once the acceptable margin of error (half-width of 0.95 confidence interval for the mean difference) is determined can plug in formulas related to precision - see e.g. . Beware: sample size needed for interaction is easily 4 times as large.

Alec Pawlukiewicz, Neuroscience and Psychiatry

Effect of exercise on neuro cognitive testing. Database of 20,000 participants - 9,000 after exclusions. Control for covariates sex, age, education level, # prior concussions. Interested in matched analysis. Not having enough controls. Suggested using full qualifying sample without matching, to maximize power and avoid any arbitrariness in how matches are determined. Non-matched analysis requires careful specification of the statistical model.

Several neuro scores are given by the test. If scales are continuous enough can use the standard multiple regression linear model if analyze one score at a time. May need to model age as a smooth nonlinear effect and perhaps likewise for education. Age and education may be co-linear. Variable of major interest is exercise (binary). Need to consider whether exercise may interact with age, sex, etc. What about type of exercise? For variables such as # prior concussions a quadratic effect often suffices.

Dillon Pruett, Hearing and Speech Sciences

Respiratory sinus arrhythmia. Comparing in children who do not stutter, stutter and persist, stutter and stop. They watch a video followed by a task, and this is repeated with different videos/tasks. Baseline re-measured at the end. Question about whether to form groups or to have a continuous-time longitudinal model with stuttering measures as the response variables (without categorization). Answer questions by estimating difference in means over time. Need to interpret the result in a clinically meaningful way. Need to adjust for baseline stuttering measure as a covariate. This might possibly be interacted with the intervention effect. Need to carefully formulate the linear model and account for within-subject correlation using something like GLS or mixed effects models (the latter is mainly used if there are more than 2 or 3 measurements over time within subject).


Omair Khan, Center for Research on Men's Health

  • "I would like to request some time to talk to another statistician about exploratory factor analysis I am doing in R with the psych package. This procedure is fairly new to me and I have some questions that I would like help with."


Mary Lauren Neel, Neonatal

  • Association between ITSP and illness severity score
  • Association between parenting style (PSDQ) and infant adoptation.

Mark Tyson, Urology

  • Bladder neck size on incontinence, controlling for BMI, age, preop score, disease status, and stitch.
  • Restricted cubic spline examples: MSCI Biostat II STATA


Dillon Pruett, PhD student in the dept of hearing and speech sciences working with Dr. Robin Jones

  • I'm working on a project involving longitudinal data with children who stutter and persist, children who stutter and recover, and children who do not stutter.


Scott Karpowicz

  • Matched design, 1:1, 1:many, BOOM
    • match on socio-economic, clinical factors, etc.
  • Change point analysis
    • see if readmission rates change at time of policy implementation
  • REQUEST FOR VICTR SUPPORT: Clinic statisticians recommend a 90 hour voucher.


Sam Gannon


  • Developing a randomized controlled clinical trial in mental literacy. Working notion, to increase mental literacy, communications which in turn increase mental health outcomes.
  • Submit concept paper to NIMental Health. Questions to address and want to get statistical expertise.
  • Questions: 4 educational arms and a control group for a total of groups. Setting community mental health clinics
Metric for outcome measure clinician reports notes - self management, behavioral adherence to protocol and rate of compliance These response measures are known to be correlated. Intervention: different educational programs. Control will have standard of care.
  • Consider cluster randomization. Figure out how many clinics that you will have access to. Five arms note one clinic receive one arm.
  • How to assess "fidelity"? Recording data consistently. Approach with assessment for some of inter-rater reliability.
How do you capture your outcome? If survey or standard form then it will be much easier to make results consistent. For example if reporting is done through RedCap, you will have the opportunity to formalize or standardize process.
  • Mediation analysis (Baron & Kenny, structural equation modeling). First you need to show that your intervention has an association with response variable. Mediator will be communication for example
. What factors mediate the intervention?
  • * (Y~X) Education is associated with improved mental health.
  • * (X~M) Education works through health literacy and/or communication(Mediators) to improve mental health.
  • Will I benefit from cross-over design? We believe that once knowledge is gained it will be difficult to have a "wash out". Cross over design will be more appropriate to a set up such the development of new drug with clear wash out.
  • Question from biostatisticians: do you need 4 arms? Can you combine some of these educational programs.
  • Transient effect: Is it common in the literacy literature and look into other clinical studies such as in diabetes which require behavioral changes. There are issues of relapse and maintaining adherence.
  • Timeline: Extend two years follow up time to address the "transient effect" although most studies have short follow up. Can you follow up subjects on StarPanel to show that you can address long term effects. Need to sit down with statitiscians to address realistically the multiple issues. How many clinics do you think that you could have access to? Recruitment time? How many subjects are needed?
  • Consider short term effects and long term outcomes. Can you design you study pragmatically without too much effort to collect data? Using the real set up Dr. entries for follow up assessment.
  • Recommendation: Follow up with VICTR voucher and statistician for help with proposal.


Heather Lillimoe

General Surgery Resident

I am currently in the process of designing a research study pertaining to resident feedback within the department of surgery. My hope is to utilize REDCap for my primary mode of obtaining data. I was hoping to meet with a biostatistician as I apply for VICTR funding for the study. It involves an educational timeout before an operation. This is a 3rd year rotation in plastic surgery. There is an iphone app to do a competency rating.
  • Survey - baseline assessment - residents and attendings - 85 questions
  • Additional survey after rotation

Cara Singer

I am a PhD student in the Department of Hearing and Speech Sciences. I would like to attend the biostat clinic today (if possible) to discuss appropriate analyses for a study I am conducting under the mentorship of Robin Jones (Developmental Stuttering Lab). The study is investigating whether a risk factor assessment (a mix of categorical and continuous variables) can predict stuttering persistence. 70-80% spontaneously recover. Would like to identify those likely to persist, in advance, for focusing therapy. Multiple risk factors have been identified. Empirical evidence for supporting predictive ability of the risk factors is sought.
  • Children previously seen - diagnostic visit; 4y ago; stuttering up to 18m; English is primary language
  • New follow-up for status at one point in time
  • Baseline variables that originate from continuous measurements (e.g., age at onset) need to be analyzed as continuous variables
  • Include baseline stuttering severity as a predictor
  • With a maximum of 150 children the maximum number of candidate predictors might be around 10 if the outcome variable is almost continuous (it's worse if outcome is almost binary)
  • Stuttering is multi-dimensional, e.g., some children may reduce amount of speaking because of the problem, so they seem to stutter less
  • May consider a compound summary of all the outcome measures, e.g., average rank across children; clinical ranking of scenarios can also be used
  • Dependent variable needs to have at least 5 frequently levels and be ordered or continuous
  • If there is one standout, popular scale, that one could be used by itself
  • Empirical variable selection requires an enormous sample size to reliably find the "right variables" so it's best not to use selection procedures; can find various approximations to the model for clinical non-computerized application
  • Data reduction methods (variable clustering, principle components, redundancy analysis) can be useful for effectively reducing the number of predictors to use in the multivariable model


Chris Brown, Internal Medicine Resident

  • To go over analysis produced by VICTR biostatisticians


Kazeem Oshkoya, Division of Clinical Pharmacology, Dr. Dan Roden's Lab

Data analysis on blood sample storage and drug concentration - look at whether a gel absorbs too much of a drug in the blood to make drug assessment accurate enough. Measured at baseline and 4h. Need to know how to describe the base value. Triplicate measurements available. More interested in relative comparison.
  • Best to present all the raw data
  • Might use 3 quartiles (25th and 75th percentiles and median) as descriptive stats and use Wilcoxon signed rank test for testing for a difference between baseline and 4h
  • There's also two types of samples - same study repeated with different samples, sample drug concentration
  • Only have 2 patients; plan to have 5 later
  • Better to not average over the 3 replicates - may hide variability
  • Bland-Altman plot (mean-difference plot) is a good way to show agreement and whether variation is stable over base levels. If band of variability expands going from left to right, this is an indication that perhaps the analysis should be done on the log concentration scale.
  • Other useful ways to summarize data: mean absolute difference between estimated and true concentrations - separately by no gel and gel
  • Can also show mean absolute differences between replicates ignoring the true concentrations
  • There are problems with lower limit of detection, representing missing values that are not randomly missing; ordinary analysis may be problematic

Jessica Dennis, Lea Davis, Genetic Medicine

Modeling lab values to look for genetic variation; data from the synthetic derivative
  • Interested in variation over time within patient
  • Variants are summarized into polygenetic risk scores
  • Difficulty in interpreting results if patients are being treated for the lab abnormality being studies
  • How to define time zero?
  • May want to ignore records corresponding to post-Rx periods
  • Started with HDL
  • Side study: confirm that med initiation that is supposed to modify HDL really does
  • Simplest longitudinal analyses:
    • Compute within-patient Gini's mean difference to correlation with gen. risk score; asks whether gen. risk is correlated with variability
    • Similar but summarize with the median to correlate gen. risk with overall height of the longitudinal records
    • Summarize entire longitudinal record with slope and intercept; AUC and relate summary measures to gen. risk score
  • Would be useful to summarize the data using representative patients after clustering on mean HDL, shape, number of observations, maximum time gap between any two measurements
  • Another type of analysis: summarize each patient using the 9 deciles of HDL; use these deciles to predict polygen. risk score
    • Does not take time ordering into account
    • Might add a slope or shape summary to the deciles


Amanda Peltier, Department of Neurology

Discuss Aims and power analysis for R01


Jake Landes, PT, DPT Vanderbilt Sports Medicine, Rehabilitation Services

  • I am a physical therapist in the Sports Medicine outpatient department and we are planning two studies that we would like to discuss. Primarily, though, we would like to discuss a prospective observational study we will be performing this coming school year with overhead athletes – we will be looking at the relationship of core strength to the likelihood of shoulder injury in overhead athletes. We plan to test the athletes’ core strength at start of their season and then collect data on injuries and time lost from playing their sport during the season. Specifically, we have questions about what our number of subjects should be in order to determine a difference and what we will need to do statistically in order to analyze the data.
  • Outcomes: number of days (or proportion) lost during the season due to shoulder injuries
  • Need information on the proportion of athletes who would get shoulder injury during a season. Sample size needed would be large if the proportion is very low.
  • Could use logistic regression to examine association between core strength and incidence of injury
  • Consider other factors that could affect shoulder injury such as the type of sport, number of years practicing, etc. These factors can be adjusted for in the regression model.
  • To calculate the sample size, need to specify the outcome, type of analysis used, the meaningful difference (effect size: odds ratio of injury upon one unit change in core strength) you want to detect, and some preliminary data on the outcome measurements (rate or variation). A rule of thumb: 20 cases of injury are needed for each factor you'd like to analyze.
  • Consider choosing a type of sports with the greatest association between core strength and shoulder injury.
  • how to quantify core strength, a single summary score?
  • A second study I am wondering about is an Anterior Cruciate Ligament Reconstruction study where we are going to compare a group of patients in a home based program versus standard care (control). We are wanting to do a feasibility study this year in our clinic, and I think it will be a prospective case-control study, or maybe prospective cohort—we also want to know about N size and analysis after ward.
  • Enroll 7 patients in one month. Feasibility study.


Katherine McDonell, Neurology

  • Parkinson's disease - norepinephrine; VICTR application
  • Original intention peripheral blood pressure support
  • Interested in a combined medication regiment
  • Goal to get nor. into CNS
  • Propose to study n=16 patients
  • Need dose titration 100mg bid -> 600mg 3/day
  • Which dose do patients tend to end up with?
  • Is a safety & tolerability study, partly dose-finding
  • Patient response that is monitored is blood pressure - minimizing orthostatic symptoms without side effects; target supine BP plus headaches, dizzyness, mania; symptoms are of primary emphasis
  • Is there an accepted symptom summary scale? If not may need to just count the number of symptoms present
  • But dose adjustments are clinical adjustments based on a symptom "gestalt"
  • Target for analysis is final dose
  • Need SD of dose; best available data will probably come from what doses are used long-term in clinical practice; we'll assume this is a stand-in for the final tolerable dose
  • Once a useful SD estimate is found, it can be used to compute the likely margin of error in estimating the population mean required dose when n=16, with say 0.95 confidence. The margin of error is the half-width of the confidence interval.
  • Would be good to know what evidence exists for the usefulness of plasma drug concentrations in estimating the final required dose


Reagan Leverett, MD, MS, Assistant Professor, Department of Radiology, Women's Imaging

  • PQI project. Two types of images (new vs. old method) were performed for each patient.
  • Examine the agreement between the two methods based on the paired data (kappa stat). Readings are ordinal values.
  • Let a few radiologists read the two sets of images in random order to study the agreement.
  • May need a couple of hundreds of patients, and a few (2 to 6) radiologists. (also want to have good agreement between radiologists, that is, readings of a certain method do not heavily depend on the experiences of radiologists).


Akshitkumar Mistry

Reserved spot for consulting with Chris F. about meta-analysis


Stephen Patrick, Assistant Professor of Pediatrics and Health Policy, Division of Neonatology

  • Mary-Margaret Fill, TDH EIS
  • Neonatal abstinence syndrome and long term outcomes
  • Merge TennCare data with educational data
  • Suggest regression model with traditional covariate adjustment unless need to do special matching (family, neighborhood)
  • Biggest assumptions: children move away from TN for reasons unrelated to potential educational achievement
  • Confounding: women giving birth to infant with NAS may tend to be different from those not having an NAS child; need to adjust for all factors related to this that might be associated with educational outcome
  • Also what is the effect of school on test scores?
  • Birth records have mother's educational level, zip code, tobacco use
  • Matching records may be challenged by mother changing last name
  • Might also look at infant and mother utilization of services, diagnosis of ADHD, etc.; cross-correlate with educational achievement


Lindsey McKernan

Here is the feedback I received on my application: Power analysis never should involve having a power of detecting a previously observed (and probably measured with bias) effect. Power should always be defined as the probability of detecting a minimal clinically meaningful effect. Also, this type of study is more suited for justifying sample size on the basis of precision of an effect of interest (usually a difference or a correlation). Precision is stated as a margin of error e.g. half-width of a confidence interval. Please revise Section E of the proposal and feel free to attend a clinic to discuss.

What was initially written: Power Analyses: Previous researchers have found moderate relationships between trauma severity and pain symptoms (r = .29; Poundja, Fikretoglu, & Brunet, 2006). Power analyses using unadjusted effect size from this study based on their sample size of 130 suggest a necessary sample size of at least 97 for the present study to reveal similar effects. Power analyses of the results of studies of the relationships between trauma severity, pain severity, experiential avoidance, and anxiety sensitivity (Gootzeit, 2014; Ruiz-Párraga & López-Martínez, 2015) suggest that a sample size of 144-158 is necessary to find these associations. The hypotheses outlined above will be tested through bivariate correlation and linear regression analyses. Specifically, relationships among variables of interest (Hypotheses 1A, 1B, 2A, 2B) will be assessed through Pearson product-moment correlation analyses to determine the strength of the association among these constructs in our sample. Tests of moderation (Hypotheses 2C, 3) will be tested using multiple linear regression with cross-products of the variables of interest to assess the interaction between predictors. All analyses will be carried out on either SPSS 22 (IBM, 2013) or the R statistical package (R Development Core Team, 2010)
  • See Chapter 8, P. 8-12 of - suggest using the r=0 curve. This approach is using the margin of error based on 0.95 confidence limits. E.g.: "With a sample size of N subjects we can estimate the correlation coefficient between two variables to within a margin of +/- xx with 0.95 confidence (see graph)."
  • Important to prioritize the comparisons and to report them in this pre-specified order so that no multiplicity corrections will be needed
  • A regression model that allows for interaction between time since trauma and amount of trauma would allow for estimation of the time-decay or enhancement of memories-effect. The time interaction effect may be nonlinear.

2015 Dec 14

Sachin Patel, Psychiatry

  • Animal model for exposure to stress, long at differential response to stress
  • Interested in susceptibility to stress
  • Measure of anxiety is a key measure (high = more anxious)
  • Each animal has a baseline measure
  • Would be good to do a Tukey mean-difference plot (Bland-Altman plot) to be sure that the delta is an adequate summary of the two measures
    • Also watch for floor and ceiling effects
  • Using the delta as a continuous stress response measure will optimize power and minimize arbitrariness
  • Discussed regression to the mean
  • Problem with choice of anxiety measure out of many
  • A composite measure may help, e.g., average z-score or average rank; can do Spearman rho rank correlation on the result, against another variable; can describe variability in ranks across anxiety measures
  • Otherwise analyses of disparate measures can be hard to reconcile

2015 Dec 7th

Pierce Trumbo

  • Shade tree clinic, where patients do not have insurance or do not have enough insurance can get medical service.
  • Primary outcomes: number of ER visits, length of hospital length of stay. Will compare before and after pts visited the clinic.
  • N=680 patients and estimate to have ~300 meet inclusion (time span between first visit and last visit greater or equal to 1 year).
  • Need estimate for VICTR application. Suggest for $5000.

2015 Nov 30th

Andrew T. Hale, Medical Scientist Training Program

  • Need a quote for biostatistical support for a VICTR grant submission
  • Want to assess the association between brain tumor grade (total 128, 93 1s and 35 2s) and gender, age at diagnosis, Edema (0-3), draining vein, necrosis, location (8 different location).
  • Will apply for VICTR voucher in amount of $2000.

Christopher John Prendergast, Tracy McGregor

  • We will specifically be seeking some guidance regarding graphical representation of data related to statin doses in children and adolescents.

Christopher Lee Brown

  • Discussed analysis for reviewer's comments

2015 Nov 23rd

Mark A. Clay, Divisions of Cardiology and Critical Care

  • The purpose of the study was to evaluate whether patients with single ventricle physiology undergoing the second stage of surgical palliation, who’s length to weight ratio was >90% were at higher risk for increased ICU length of stay, ventilator times, and increased non-invasive ventilation when compared to those whose length for weight was <90%. Analyzing the data with the Mann-Whitney U Test there was a statistically significant difference between ICU length of stay and ventilator hours for those with weight for length >90% compared to those <90%. However, I attempted to analyze the data again with Spearman’s to see if there was a correlation between increasing z-score percentile and there was no statistically significant correlation.
  • Clinic question: Has the data been analyzed appropriately to answer the question? Should I be concerned that Spearman’s correlation did not show a statistically significant correlation between the variables even though there was a statistically significant difference between the groups? Should I use and how might I best demonstrate association or risk related to weight for length z-score >90% with linear regression?

Rebekah Griesenauer (Conley), Biomedical Engineering

  • I am designing a study for a small group of human subjects to test the feasibility of a new tool that I designed for breast cancer assessment using medical images. I would like some guidance on effective study designs for a small number of patients and for determining the accuracy of a new tool when there is no current clinical equivalent to compare to.
  • Need a measureble outcome to calculate the required sample size

2015 Nov 16th

Aaron C. Shaver, M.D., Ph.D. Assistant Professor of Pathology, Microbiology, and Immunology

  • The csv consists of sample ID, the covariates I want to test (age as an integer and categorical variable; poor.risk through transcription, which are all categorical variables; and num.muts, which is an integer) and the OS and PFS data (for censoring rows, 0=censored and 1=dead). I would like to include the interaction between age and poor.risk, because I have biological reason to believe that that interaction is relevant. My questions concern: measuring goodness of fit of the model; how to interpret the interaction term; how to estimate power, given the large number of covariates and small sample size

2015 Nov 9th

Fernanda Maruri

  • "If possible I would like some help interpreting results of 2 Wilcoxon Rank Sum tests in which one is significant and the other is not."
  • Compare

Jessica Kaitlin Campbell

  • The goal of the project is to examine the impact that the palliative care unit has had on the medical intensive care unit in terms of patient length of stay and mortality. I have collected data regarding some parameters per and post opening of the palliative care unit. I am interested in the best approach in analyzing the data.
  • Have data a year before and a year after the unit opened. Want to compare LOS and mortality in MICU. Both groups had palliative consult, only some patients after went to the palliative care unit.
  • Wil apply for VICTR biostat support. Suggest for $5000 study.

2015 Nov 2nd

Gabriella D. Cozzi

  • GDM project analysis. Associaion between hoursehold income and education with the five primary endpoints.

Michael Chomat

  • To discuss experimental study design and data analysis for a project within REDCaps
  • Two REDCap data base can be merged based on common identifier.

Jamie Robine

  • R questions about fitting logistic regression model and plotting the figure.

2015 Oct 19th

Rebecca Cox, Psychology

  • I am working with the data from the National Comorbidity Survey Replication, a nationally representative sample used to estimate prevalence rates of psychological disorders. I have questions about what types of analyses to use with a complex sampling design that includes strata, clusters, and weights.

*Suggest review survey document to specify correct strata, clusters and weightings variables *Set up complex survey design effects in SPSS complex module.
  • Subpopulation command in stead of subset anaysis.

2015 Oct 5th

Stevenson, David, Health Policy

  • Stratified cluster randomized trial.
  • Intervention group: predicted mortality risk score obtained for all the patients, based on which "top patients" will be provided with hospice and will be expected to get better life quality. Control group: standard care. Individual agencies (50 in total) will be randomized to intervention/control group.
  • Cutoff of risk score may vary within and across the sites. Information obtained from prediction model: median life expectancy, probabilities of death during certain time periods.
  • If the primary outcome is continuous, would need SD to calculate sample size. If it's the time to event, we will need expected median time in each group.
  • Consider the flexible/sequential design, having pilot sites included in the final analysis.
  • Biostat resources: VICTR Voucher (35 hours). Dr. Matt Shotwell

Conor McWade, ED, PhD student

  • Apply for Voucher (90 hours)
  • Have collected car collision/victims data, demographics of the passengers, road characteristics.
  • Define collision as fatal vs serious.
  • Aim to develop a prediction model to predict the severity of collision based on location, time, etc.

2015 Sep 28

Katrina, Electrical Engineering

  • Our study is on incidence of eye disease seen at Vanderbilt. We have data on 33,000 patients looking at incidence of disease and I would like to discuss how to best analyze this data.
  • Whether incidence at Vanderbilt can represent incidence in Nashville


  • I was hoping to come to biostats clinic today to get some help with sample size calculations for my project.
  • Cross over design. Within subject correlation is 0.7. Need power calculation.

Wes Clord

  • Power analysis of survival analysis

2015 Sep 21

Jose A Arriola, PGY 3 - Psychiatry

  • "I plan to implement a different type of interview in the first episode psychosis outpatient clinic at Vanderbilt Psychiatric Hospital and investigate how it contributes to improve adherence and management. The type of interview is called Shared decision making approach which is a little bit different to what we are used to. I am planning to train the MDs and providers on this technique and then compare measurable outcomes before and after the training. The outcomes would be no-show clinic rates, hospitalizations, etc. (things that are recorded automatically on the patient's chart). "
  • Statistical tests: Wilcoxon signed rank test for continuous outcomes, and McNemar's test for binary outcome
  • Primary outcome: number of times that pt did not show up within 3 months raning 0-4. Proportional odds logistic model to analyze. num of no show after intervention ~ number of no show before intervention + age + gender
  • Sample size calculation: use PS for paried binary outcome

Tamara Moyo, Hem/Onc

  • Want to correlate the resistanze to therapy based on imaging with cell signal
  • Wilcoxon signed rank test (paired t-test); Wilcoxon rank sum test (two group t-test)
  • Mixed-effects model to adjust for other covariates.

2015 Sep 14

Mhd Wael Alrifai, Neonatology

  • Name of project: Paretneral Protein Calculator (PPC)
  • Type: Randomized controlled clinical trial, un-blinded
  • Help needed: Discussing the primary and secondary outcomes, designing the database
  • Study status: IRB approved, enrollment starting next week
  • Research question: the effect of intervention on the accuracy of protein prescription. The primary endpoint is the ratio of target days to total days (target days are the days when prescriptions are given with correct amount).

Sudipa Sarkar

  • My research topic is on the effect of statins on non-alcoholic fatty liver disease
  • retrospective cohort study.

Michael C. Dewan, Department of Neurological Surgery

  • I am interested in discussing sample size calculations. We are conducting a clinical trial evaluating the effectiveness of a prophylactic antiepileptic drug (levetiracetam) in brain tumor patients. For 14 days following surgery, patients will be randomized to either drug or no drug. The primary outcome is the development of a clinical seizure and the follow-up time to primary endpoint is 14 days.


  • I would like to address a few questions regarding sample size calculation for a translational study on the role of alternate complement activation in sickle cell lung disease

2015 Aug 24

Christopher Brown

  • Retrospective cross-sectional study on heart failure patients.
  • Outcome is the low potassium, related to urine output per hour.

Maya Yiadom, Emergence Medicine

  • Criteria for giving EKG to diagose STEMI.
  • Trigger criteria: typical symptom, atypical S

Megan Pask, Tricia Russ, BME

  • Compare CT values between four groups.
  • Use non parametric test: Kruskal Wallis test (ANOVA), Wilcoxon Rank sum test (two sample -t-test)

2015 Aug 17

Karl Zelik, Assistant Professor of Mechanical Engineering, Assistant Professor of Physical Medicine & Rehabilitation

  • sample size calculations for a grant proposal

2015 Aug 3

Lan Wu, PMI

  • Had questions about VICTR proposal review. Suggest use Wilcoxon Rank Sum test or Wilcoxon Signed Rank test to compare between and within subjects
  • Try to identify subset of b-cell in this set of subjects - will be able to provide descriptive statistics
  • Consent 60 subjects will estimate to have 30 subjects. Will quantify b-cell and compare b-cell among two different locations. First get a percentage of b-cell of the mixture then calculate the absolute number of b-cell per gram tissue.
  • Will find SD from preliminary data and calculate required sample size based on that.

2015 June 29

Aaron Noll, VMS IV

  • retrospective chart review of 1750 patients. correlation between screening exam results with 15 diseases. 923 patients had actual visits within two years (gold standard of disease).
  • Analysis data set: two-by-two tables based on 923 patients. Compare demographics between 923 patients with (1750-923) patients.
  • I am currently finishing a research project that is regarding various diagnoses that are able to be picked up on a screening exam (for diabetic retinopathy). To this point, I have calculated the following values for the 16 diagnoses of relevance: true positives/negatives, false positives/negatives, positive/negative predictive values, and sensitivities/specificities. However, I am unsure what the best test is to determine statistical significance or importance of these numbers--eg, do I use a 95% CI, odds ratio, etc. One issue with these results is that although I have a very large sample size for the initial screened population (over 900), many of the diagnoses have less than 5-10 true positive results.
  • Zero or close to zero number in certain cells. Wilson confidence interval. binom.confint() of binom package.

2015 June 22

Nelleke van Wouwe, Department of Neurology

  • We are working on a grant and we have some questions about a power calculation for a Repeated Measures ANOVA (based on effect size from a previous study).

2015 June 15

Daniel J. Miller, Department of Psychology, Psychological Sciences

  • Discussion about microstimulation data to develop a test of the hypothesis that stimulating two areas in the brain from which evoked movements differ produces a blend of those movements (endpoint neuronal encoding)
  • Need help understanding how to organize the data in order to build a model to explain physiological results (e.g., how the dual stimulation sites interact)
  • Suggest apply for a $5000 VICTR voucher.

Kendall Anne Ulbrich, Pediatrics

  • I am requesting assistance in figuring out statistical significance. We see a trend in the data with the diagnosis of chronic lung disease leading to increased risk of death after trach placement vs other diagnosis.
  • Babies in NICU, outcome is alive/died, want to compare chronic lung disease to other diagnosis.
  • There were ~15 diagnosis, among whom 12 had chronic lung disease.
  • Total 115 babies (25 died in NICU). Primary outcome is the death in NICU. 8 (or 11) babies who had lung disease and died.
  • Plot Kaplan-Meier curve first for description, use log-rank test.
  • Can use Cox proportional hazard model to analyze the association between lung disease and survival in NICU.
  • Could also apply for a $2000 VICTR voucher.

2015 June 1

Robert Lentz, cardiology

  • My project is looking at radiation-induced atrial fibrillation, specifically in patients with breast and lung cancers. I have raw data extracted from the Synthetic Derivative and am hoping for some guidance regarding my data analysis plan and how I might be able to best display my data.
  • There are ~3000 breast cancer pts (125 had AF), ~2000 lung cancer pts.
  • To test the association between radiation and AF, include all pts (y=AF, x=radiation y/n, cancer side); then take subset of pts who had radiation, fit a model of radiation dose/side with AF.
  • Length of follow up is different for all patients. Can use survival analysis. If certain proportion of pts died before developing AF, should treat those pts as competing risks events.
  • If apply for VICTR voucher, suggest $5000.

Robert K. Tunney, Jr., Cardiology Resident

  • Email: My research is investigating statin dose intensification according to the ACC/AHA 2013 Cholesterol Guidelines in post-ACS patients. I am interested in performing logistic regression analysis on ~300 patients and potentially Spearman rank r correlation coefficient.
  • Two groups: historic control and intervention group. Binary outcome. Primary aim is to assess the outcome difference between groups.
  • Chi-sq test and multivariable logistic regression can be used to test the primary hypothesis.
  • Suggest propensity score adjustment.
  • Will apply VICTR voucher in amount of $2000.

2015 May 11

Zac Cox, PharmD, BCPS

  • Email: I would like to request a biostats clinic reservation on Monday 5/11 from 12-1 for a comparative effectiveness research project. The main question is selection of the best primary outcome to maximize power for a population size that will be fixed (secondary to funding and patient enrollment). Our second question is the best statistical analysis method for 3 independent continuous variables (ANOVA vs the 2 experimental groups independently compared to the standard of care comparison arm). Please let me know if you would like me to send anything in advance.
  • Use continuous outcome to maximize power
  • Wilcoxon rank sum test to compare two new treatment groups to the standard care group
  • Multivariable linear model adjusting for baseline weight and treatment regimen

Michelle K. Roach, Obstetrics and Gynecology

  • Email: We will be completing a retrospective chart review looking at pregnancy and delivery outcomes in women with gestataional diabetes. We plan to use RedCap database for data entry.

2015 Apr 27

Aaron Noll, Medical Student

  • I am a third year medical student working on a research project that is evaluating the teleretinal imaging program at the Nashville VA Hospital. I attended one of your clinics about a month and a half ago and greatly appreciate the help I received at that time. I have now completed my data collection and am moving on to the data analysis portion of the research, and would like to discuss my revised project with you to see what the best way is for me to proceed.
  • As an overview, I am looking into the teleretinal screening program to evaluate its efficiency and its accuracy at diagnosing abnormalities other than diabetic retinopathy (the true purpose). I have recorded the data on the following topics:
    • Demographics (Age, sex, ethnicity)
    • Months from consult entry to screening
    • Days from screening until note loaded to chart
    • Screening diagnoses, diagnoses found at subsequent visits, and diagnoses found at previous visits
    • No-show rate for the screenings
    • Consult timing
    • Months since prior screenings and clinic visits
  • Had imaging readings and clinic diagnosis on ~1700 subjects. There were 18 diagnosis categories, looking at their agreement.
  • Will apply for VICTR voucher. Suggest $2000 for up to 35 hours

2015 Apr 20

Lexy Morvant, Pediatric

  • NICU data analysis
  • time trend of gestational age when receiving ECMO (Y2004-2014) for C-section babies. To evaluate the effect of policy change (increase gestational age for C-section baby in 2007) on ECMO.
  • Only have the information on birth year available. Fit a linear regression model
  • Also have the information on the total number of all ECMO babies. With an assumption that the proportion of C-section babies remains the same, could fit a poisson linear regression model.

2015 Apr 13


  • I have a retrospective dataset of patients who underwent a new cochlear implant programming procedure. The data contain pre- and post-intervention objective performance data, demographic data, and information about the cochlear implant type and location. I am trying to develop model(s) that can answer the following questions: 1) How can we predict whether a patient will be a responder to re-programming? 2) Which variables are most predictive of change in performance from baseline?
  • 177 patients.
  • Endpoint: measurement performance (0-100)
  • Predictors: 15 ~ 20
  • Fit a multivariable linear regression model. Predictor importance can be measured based on the model.

2015 Mar 9

Taylor Leath

  • We attended a biostats clinic on February 23rd to develop a statistical plan. Now that we have a dataset completed, we are having difficultly with our regression models and would appreciate your input.

2015 Feb 23

Katie Rizzone, M.D., Clinical Instructor, Orthopaedics and Rehabilitation

  • I would like to request a methods clinic (to review my methods) for a retrospective chart review study on female college athletes and stress fractures I am writing an IRB for.

Taylor Leath

I would like to reserve a time on Monday, February 23rd to develop an appropriate statistical plan for our study and dataset. I've attached the study protocol which details our specific aims and hypotheses. Our primary questions: 1) Is linear regression the appropriate model to use? Predictors would be sex, age, years of education, participant's current health, trauma exposure and religiosity (all continuous except for sex), and the outcome variable would be each of the individual health states (GOSE 2-8). If so, this would mean six different regression models for the six health states? 2) Alternatively, would it be more appropriate to develop one regression model that includes the health state (GOSE 2-8) as an additional predictor? 3) Do we have sufficient sample size to answer our study questions? Current n=2156 after exclusions. 4) We would also like to show whether the utility values for each of the six health states are significantly different from one another-- would that simply be a within-subjects ANOVA with pairwise comparisons? 5) Should we consider transforming the worse-than-death values?

2015 Jan 12

Dr. Heidi J. Silver, Ph.D., R.D Research Associate Professor of Medicine

  • Study of diet intervention, body composition, insulin resistance, lipo.
  • Could apply for a VICTR voucher of $4000.

2014 Dec 15

Amos M. Sakwe, Department of Biochemistry and Cancer Biology, Meharry Medical College

2014 Nov 24

Paula DeWitt, Center for Biomedical Ethics and Society; Madhu Murphy, pediatric cardiac intensive care unit

Email: We are wanting to test the effectiveness of a “journey board” (see attachment) designed to better prepare parents for their child’s stay and eventual discharge from Vanderbilt’s pediatric cardiac intensive care unit. This will entail giving self-administered surveys with preparedness and satisfaction items to parents of children hospitalized in the pediatric ICU immediately before and immediately after the parent has been exposed to a 15-minute educational intervention using the journey board. The intervention will take place in the child’s hospital room (or another room in the unit) and will consist of a clinician walking the parent through the journey board, and answering any questions the parent may have. Immediately before this, a researcher (not the clinician) will approach the parent, explain the study, and ask if the parent would like to participate. If yes, the parent will be given two short (5-10 minute) self-administered surveys to complete. He/she will be asked to complete one prior to the intervention and one immediately after the intervention. The data will be used to assess the effectiveness of the journey board in preparing parents, and we would like your advice concerning numbers of parents we will need to interview to obtain statistical significance and statistical techniques to be used.
  • Frank's note: Design is confounded with time/fatigue/learning. Also there is little precedent for doing a pre-post study with such little time between pre and post. I think you will need to do a randomized study to attribute any effect to the intervention. Randomize 1/2 of families to get the intervention, 1/2 to get the prevailing treatment, and give survey at the "after" time point for both groups.

Amma N. Bosompem, MPH graduate student

I'm a graduate student in the Masters of Public Health program who will be looking at treatment completion rates in a specific population for my thesis. I need some guidance on study design and what analyses are appropriate given my sample size.
  • TB clinic in Nashville, 203 cases (information on case only) in year 2013.
  • Treatment: completed vs not completed (refused, lost to follow up, etc.)
  • Research question: 1. treatment completion rate. 2. the association between patient's characteristics and treatment completion.
  • Prepare data set as
  • Is there an association between country of origin and acceptance of treatment (accepted vs. no accepted)
  • Apply logistic regression analysis (dichotomous or binary response variable) and include the variables of interest. *General rule of thumb the smaller sample size/10 will help you assess your regression power or how many variables you can include in regression model *N=48 that refused and will be the limiting sample size in regression analysis
  • Country of origin main factor and will have to think of best way of grouping *The covariates of interest: Age as continuous non-linear; gender, marital (married vs. non-married) and country of origin

2014 Nov 17

Mary Van Meter, medical student

My project is looking at the cost of packaging surgical instrument trays (possiblly using regression) and calculating the percentage of instruments used in trays across various Gyn specialties.
  • Total number of instruments used per tray (25-100), usually less than 50% is used.
  • Will compare unnecessary cost between specialty
  • Will apply for VICTR voucher. A standard $2000 is appropriate.

Tomas DaVee, GI

  • Patients underwent liver transplant who had plastic stent to treat leak, about 20-30% needed mental stent later
  • Want to predict early whether patient needs mental or not so pt does not need to surfer pain
  • The current data only gives conditional needs to mental if had plastic already
  • Suggest do descriptive statistics and plan bigger study to develop prediction model
  • Use R for internal validation and calibration using bootstrapping method (rms package)

2014 Nov 3

Monica Ledoux

I am an adjunct at Vanderbilt's Dermatology department, working with Zhengzheng Tang from biostats on microbiome and skin and would like to know the biostat budget for VICTR application(s)
  • Want to know the relationship between Cortisone treatment and bacterial change.
  • Each subject will be his own control: cortisone on one arm and no cortisone on the other. Each arm will be tested at two sites, one normal skin and one tape stripping skin. Observe bacterial change. Therefore, each subject will have 4 tested samples and each sample measured twice (total 8 per person)
  • Look at treatment effect on normal skin. Suggest amount of $2000.

2014 Sept 29

Bryan I. Hartley, MD Department of Radiology

Review an abstract.
  • There are limitations of pre post design. Many factors will affect the outcome besides the intervention like time.
  • Box plot with raw data to explore the distribution
  • Can use Wilcoxon signed rank test to compare continuous outcomes before and after
  • Consider ANCOVA (Analysis of Covariance) to analyze post while adjust for pre specified covariates like previous experience

Adam P. Bregman, MD, MBA, Annette Ilg, Vanderbilt Internal Medicine Resident

I am starting a project with Dr. Anthony Langone in the renal transplant division and I have come across some questions with my redcap database that I would like to ask
  • Retro spective study of post renal transplant patients. Follow those patients for two years to observe a rare event.
  • Describe users characteristics. Some pts took medication for entire 6 months, some stopped prior 6 months for certain reasons, some retook it later. Can consider using certain amount of time to define user.
  • Binary/categorical variables can be described as frequency and percentage

2014 Sept 22

Kasim Ortiz, Sociology, Doctoral Student

I’d like to obtain assistance with a project I completed this summer while attending the Fenway Summer Institute on LGBT Population Health. I used a dataset that permitted me to analyze the effects of states having restrictions on same-sex marriage on smoking, extending previous work by examining these impacts among racial minorities that are sexual minorities. My findings were counterintuitive to previously published work, insofar that I did not find where states restricting same-sex marriage policies had a negative impact on smoking among racial sexual minorities. The dataset is the Social Justice Sexuality Project.

Most previously published papers examining the effects of same-sex marriage policies on the health of LGBT populations have utilized GEE in their statistical analyses. However, I was having problems getting GEE models to converge with the cross sectional data I am using and thus I utilized GLM for the binomial family with adjustments for state clustering (vce) using STATA. I’m apprehensive about wrapping up the manuscript out of fear that I might get a ton of push back because of the methodology I’ve chosen. Also I was having problems with conducting post-hoc analyses on some of the interactions that I included in the analysis (sample sizes kept changing across models preventing me from appropriately conducting likelihood ratio tests). Thus, I wanted to obtain statistical advice on the differences between the two and consultation on how I should proceed. It is my understanding that R is utilized predominantly and since I am learning R in my statistical sequence this year, I thought this would be a great opportunity to compare STATA with R on a question that I’m invested in and a dataset that I’m familiar with.

  • Logistic regression model with robust standard error is appropriate.

Robin Jones, Assistant professor

We have two questions regarding our project on respiratory sinus arrhythmia (RSA) and stuttered disfluencies.

We are interested in how that RSA (respiratory sinus arrhythmia, parasympathetic activity on the heart) is associated with speech disfluencies. RSA during various tasks is influenced by it’s baseline value, thus, in our model we need to account for baseline RSA in some way. When running a model predicting stuttered disfluencies with RSA during an emotional speaking task, should we use a) RSA during the task and RSA at baseline as covariates, or b) create a residualized difference score of RSA during task relative to baseline as a covariate? With option “a” we would be using two covariates in the model (RSA during task & RSA at baseline), whereas with option “b” we would be using only one (RSA difference score).
  • Could include baseline RSA if colinearity is not an issue.

I would like to verify that our analytic plan is the most appropriate. We are using a Generalized Linear Regression with Negative Binomial Distribution. We are using this distribution because stuttered disfluencies are not normally distributed.
  • Generalized Linear Regression with Negative Binomial Distribution is good.

The model for our 3rd research question: Is the relationship between RSA and stuttered disfluencies different for children who stutter versus children who do not stutter during various emotion conditions. We used a 3-way interaction (as well as the various 2-way permutations) to assess this: RSA*Group*Condition. However, when running this model, I am not getting an intercept term. Is the model flawed in some way?
  • Probably.

Lastly, we have some outliers, and I am curious on what is the preferable way to handle this: 1) next case imputation, 2) log transformation, 3) square root transformation?
  • Not a real outlier

  • Take into account the correlation within each subject.
  • Might have carry-over effects between different periods. Could test on equivalent carry-over effects.

2014 Sept 15

Stacy S. Klein-Gardner, Ph.D. Biomedical Engineering

My data that I would like to analyze is survey data from a Likert scale that I believe to generate interval data. I am attaching the raw data to this message. We can focus only on the first spreadsheet as the analysis will be the same for all. The data that is shown includes pre and post data from each of three summers of educational intervention. On the spreadsheet, I have entered the data first under the summer in which the intervention and testing took place. I then moved the data around to indicate which summer it was for the student. For example a student may have come first the first time in 2012, 2013, or 2014. She may have come only one summer or she may have repeated the program. My question is: Does repeated participation in the intervention improve the impact that the program has on the measures indicated by this survey? Is one summer sufficient to increase this measure?

  • Outreach for engineering education
  • looking at data from engineering camp for girls (looking for changes in self- efficacy)
  • self- efficacy- feeling that you can accomplish something in your life (this scale has been validated) * some girls have participated for one year, some have participated for about three years
Research Question: * interested to see if there is an effect of attending camp for more than one year
  • also interested in differences in pre-post scores for the one year attended by student
  • descriptive statistics: consider summary statistics across different categories (i.e. pre and post scores by different school types, year of study, grades)
  • Consider repeated measures type analysis (longitudinal data analysis) for assessment of slope over time (year) of self-efficacy variable
  • Per question of interest - may need to reformat data to “long style or vertical format” (i.e. have row 1 id=1: 2012 post self-efficacy score, row 2 id=1: 2013 post, row 3 id=1 2014 post self-efficacy score, etc. for each of the girls)
  • adjust for age , school type (consider the role of additional potential confounders)
  • Consider applying for VICTR funding– for assistance in repeated measures type of analysis.
  • Need to account for the correlated nature of data and verification of assumptions (such as in Mixed effects modeling or generalized least squares)
  • Account for the missing data

  • Limitation: lack of control group (there is no way to conclude that the program is the only thing that is improving self efficacy)

  • pre vs. post score for any given year
  • consider doing boxplots for each of the pre and post scores for each year (these can serve as your summary statistics)
  • Univariate analysis: Wilcoxon Signed rank test to see if there is a difference between the distributions of pre and post scores (data in horizontal format works)
  • cautioned combining the pre-scores over the three years, and post scores over the three years (year may be a confounder and impact trend of the data)
  • pre vs. post study (may see a difference, however no guarantee that improvement is from the program- not an randomized controlled trial)
  • Motivated and selected group of girls and may have higher self-efficacy baseline score (pre) - consider comparing self-efficacy scores with those reported in other studies among girls.

2014 Sep 8

Jason Winnick, MPB

I perform biomedical research where the sample sizes are generally approximately 6 to 10 per group. The primary outcome variables are usually something like counterregulatory hormones levels, glucose in fusion rates, or muscle glucose uptake. I will be submitting an R01 in October and I would like to receive your advice regarding the estimation of sample size. In addition there may be the potential to receive statistical assistance over the life of the grant if it's funded.
  • Two primary endpoints; one with greatest variability (glucose infusion rate needed to maintain desired blood glucose level) has most variability and hence will be conservative to plan for
  • 10 with type I DM 10 without
  • Other covariates: age, insulin required to maintain blood glucose, HbA1c
  • Baseline liver glycogen assessment
  • Start with 3 hour fructose infusion to stimulate liver glucose update vs. saline infusion (randomized), then insulin infusion then 2 hour period where become hypoglycemic (using clamp)
  • Need a good estimate of the standard deviation across patients for infusion rate - use the dog data taking all relevant time periods and stratify by liver glycogen to compute 12 SDs; then we can compute an averaging by averaging the variances and taking the square root
  • Need clinically relevant difference (in mean infusion rates) not to miss: estimate 1 mg/Kg/min
  • Language for grant application something like: The power calculation was based on a 2-sample t-test without covariate adjustment for HbA1c, age, etc. The actual statistical test will be ANCOVA adjusting for these factors, which will increase the actual power a bit (increase would be more had the sample size been larger; the sample size chosen has a penalty for estimating the effects of the baseline covariates).
  • Last aim: most general way to assess to to fit a smooth function of time to the longitudinal (serial) measurements, separately for each of two groups, and test for differences in shape of the two curves. A convenient choice is to fit a quadratic function of time to each curve. This increases power over individual time point tests. Suggested statistical method: generalized least squares or mixed effects linear model.
  • Suggested contacting Li Wang to tell her that a VICTR voucher is in the works

2014 Aug 18

Cecilia Di Pentima, Zachary Willis, Pediatric

  • Want to assess the impact of the implementation of an ASP on antibiotics use.
  • Monthly antimicrobials (AMs) use in days from 2009-2012 April. Data from many hospitals including Vanderbilt. Want to compare Vanderbilt to ALLCHA.
  • ASP intervention started 2012 March at Vanderbilt. Can see less use of AMs after intervention.
  • The comparison of pre and post might be biased by other factors like time not just by intervention. Institution effect is hard to assess since all institutions started intervention at different times.
  • Also needs to adjust for other factors like date for seasonal effect.
  • Linear model of VCG ~ intervention + rcs(time)
  • Better to have individual data for all the hospitals which had both pre and after data to assess intervention effect using mixed-effects model, or just compare between hospitals using data after intervention to see whether Vanderbilt does better than others
  • Consider get Vanderbilt rank among all CHA

2014 July 21

Kelvin Moses, Urologic Surgery

  • wants to do a pilot study to get 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.
  • about 3200 men enrolled. max follow-up 10 years. about half finished the whole study period.
  • Prediction of screening frequency by baseline characteristics. Association between prostate cancer stage and frequency of screening.
  • all patient self-reported data, at 5 year and 10 year. (have you had screening within the last year?)
  • GEE model of screening frequency (recent screening yes/no at 5 year, 10 year) on age, race, interaction between age and race, ...
  • Ordinal logistic regression model of prostate cancer stage/grade on screening frequency (need be carefully defined) prior to diagnosis. Need consider different follow-up of the patients.
  • Contact Li Wang( for budget estimate.

Austin Beason, summer medical student, MOON Group

  • How can I determine the required sample size (i.e. number of subjects or raters) for interval estimation of the Kappa statistic for an intraobserver and interobserver study with multiple raters? Our number of subjects is currently 20 (N=20) and our current number of raters is 27 (n=27). Further, we are hoping the given sample size will give at least 80% power at the 0.05 level of significance (two-sided).
  • >library(kappaSize)


April Barnado, Leslie Crofford, Division of Rheumatology

  • Email: I am submitting an early career grant for a starter type project due August 1 and needed help with performing and writing up power/sample size calculations.
  • Specific Aim #1: identify group of lupus patients of about 1135. Lupus nephritis patients of about 400. Nephritis is severity indicator.
  • Specific Aim #2: Determine the association between ED use and meeting standards of quality of care in management of SLE and in the treatment of SLE nephritis, as defined by the Quality Indicator Set for SLE. For aims #2, I would likely be performing Chi squared tests comparing 3 groups (non, occasional, and frequent ER users) for most of those sub-aims.
  • Specific Aim #3: Determine the association between ED use and corticosteroid use in SLE and SLE nephritis. For aim #3, I would likely be using multiple linear regression.
  • For binary outcomes, use logistic regression with adjustment of other confounders.
  • Ratio will be treated as continuous variable and will be analyzed using general linear model.
  • Hypothesis: more ED use will have higher steroid dose. Will analyze current steroid dose and #ED visits in the past 12 months. Steroid dose will be a ordered categorical variable with 4 levels. Can use Chi-square test. Proportional odds model can be used to adjust for other confounders.
  • Grant due Aug 1st, need to be done July 21st.

Taylor Leath, Trauma in Surgical Sciences

  • Survey on quality of life (N=1000). There are 7 GOSE questions about health states (0-100). Can describe the distribution for each GOSE. Predictors include gender, age, and years of education.
  • Want to compare between GOSE scores. Multiple comparison issues (21 comparisons).
  • Can use mixed-effects model taking into account of within subject correlation.

Ola Oluwole, Medicine

  • N=36 patients who had CLL transplant with two types (8 vs. 27). Want to compare survival between two groups.
  • Time from transplant to death or relapse. Sample size is limited. Mainly descriptive. Want to write manuscript.
  • Can apply for voucher of $4000.


Neelam Patel, Medical Student

  • I am fourth year medical student doing a project for dermatology. We are doing a meta-analysis of pediatric vitiligo patients to assess which populations need thyroid studies performed. I have a spreadsheet of the data. I need help analyzing it.
  • Research question: the percentage of thyroid abnormalities in pediatric vitiligo patients.
  • Only have aggregated data. Could have an overall estimate of percentage. Also could explore the variability between studies.
  • Apply for a $2000 Voucher.

Tyler Kendrick, Anesthesiology, Medical Student

  • One-year prospective study. Will record the numbers of surgeries in Ethiopia (an African country) and the number of perioperative mortalities.
  • Sample size calculation to reach a desirable precision of mortality rate estimate.


Wei Xie, Computer Science, Brad Malin, DBMI

  • we want to find out if the IRLS estimation algorithm is reversible -- e.g., given only the Fisher information matrix and scoring function (and \beta coefficients), can we go back to the original Y or X matrices
  • Context is confidentiality with data coming from multiple sites, with each site's data maintained independently, and controlled
  • How to do model diagnostics without residuals?
  • Does the distributed computing model lead to good statistical modeling practice? E.g.: covariate transformations, Y transformation, normality of residuals [could compute residual vector separately by center and share an ECDF of the residuals)
  • How often are practitioners of distributed statistical analysis assuming linearity of covariate effects? Being careful about transforming Y or modeling Y robustly?
  • Can't reverse the process to solve for an individual's datum if model is full rank, n > p, no parameter is devoted to only one subject, residual vector is secret
    • If a single parameter is devoted to 5 subjects at one site, may possibly be able to solve for a summary statistic for the 5 (e.g., race has 4 levels and one of the levels only applies to 5 subjects at a site)
  • May be able to discern that one site has an overall better level of Y than another site
  • Not able to get a robust sandwich covariance matrix estimator if residual vector is not provided; sandwich estimation requires U matrix not just U vector
  • Even if residuals are available, it may not be possible to work backwards to an individual from a given site because estimates come from a global beta vector over all sites
  • We seldom use OLS with health care data; the need for weighted X'X (X'VX) instead of X'X as used in OLS makes the identification problem more difficult in general, because V is a function of the current beta estimate (for all sites combined)
  • Worthwhile working out the special case where Y is binary and there is a single X that is binary or polytomous, and there is no special knowledge (e.g., k subjects are of type x and all have the same Y)
  • Worth taking another look at data squashing

Neil Templeton, Engineering, CHBE

  • Metabolic flux analysis
  • Rate of metabolite turnover
  • Which metabolic phenotypes are produced in high titre-achieving production processes
  • Protein therapeutics; cost of production
  • 14 conditions (cell lines); correlations between fluxes (80 reactions- flux, mass spec); looking for up-regulation
  • 80 Spearman rank correlations x 14; each correlation 10 observations (clones)
  • Two controls; secondary controls
  • Independent experimental units: clones, manipulations of cell lines
  • See if a unified model would be a better approach than pairwise analysis
  • Must be able to precisely estimate a quantity such as a correlation coefficient in order to be reliable in picking "winners" across reactions
  • Low precision (low number of independent experimental units) implies low probability of selecting the optimum reaction/condition
  • Dimensionality is high enough that an "omics" method may be needed
  • Recommend contining discussion at a Tuesday or Friday clinic


Elizabeth Morse, RN, MSN, FNP-BC, MPH Vanderbilt University School of Nursing

  • My project involves survey data of 220 Spanish and Arabic-speaking patients in the Center for Women's Health. I've completed all of the descriptive statistics but need help with the correlations. For example, I know from having surveyed patients myself that those patients who reported speaking "Arabic only" at home were more likely to self-report speaking English "not very well", but I don't know how to express this statistically.
  • To test association between two variables A and B,
    • If A is a continuous variable and B is categorical variable, use Kruskal Wallis test (or Wilcoxon rank-sum test)
    • If A and B are both categorical variables, use chi-square test
    • If A is ordinal variable and B is binary, use chi-square trend test
    • If A and B are both continuous variables, use spearman's correlation coefficient.


Brett Byram, BME

* Clinical image degradation with ultrasound
  • What are major factors of degradation? Pulling apart mechanisms.
  • Clinical target: liver tumors/biopsy; visualize needle
  • What is the best study design?
  • Ask trained readers to assess utility of image
  • Discusssed hypothesis testing vs estimation study
  • One estimand could be the mean absolute number of levels different
  • Can relate an ordinal measure to quantitative measures of image quality
  • Can estimate # patients needed if have a reliable estimate of the standard deviation of an absolute difference of interest
  • May consider progressively ruining an image to see when it becomes uninterpretable
  • One goal is to develop a model to predict expert's quality rating from multiple quantitative physics-based measures
    • May consider an ordinal response model / multinomial model


Steve Kahn, General Surgery Fellow

  • can't arrive before 1pm on Wednesdays, so attending Monday clinic
  • "I am going to perform an email survey of surgical residents (approx 5500 in the US) and wanted to know what you think an appropriate response rate would be and the best method to do statistical analysis (rough draft of survey attached). Or should the questions be revised to facilitate a better statistical analyisis?"
  • make the variable as continuous as possible using sliding bar

Philip Budge, Fellow, Division of Infectious Diseases

  • grant proposal relating to the development of new diagnostic technologies for neglected tropical diseases

LIsaMarit Wands, nursing

  • Survey on two cohorts, VA-based cohort and university-based cohort.
  • Outcome: global physical and mental health score. Pain is part of global score, and also a barrier to level of reintegration success. Could calculate a global score without pain. Could examine how pain correlates with reintegration and outcome.
  • A specific question (meaning of life) in two standardized questionnaire. Could include both in the model predicting outcome.


Stephanie Fecteau, Psychiatry Post-Doc

  • Cortisol measures 3 per day
  • % of increase because times not noted accurately
  • Need Bland-Altman plot to check proper transformation: post - pre vs. (post + pre)/2 or log(post) - log(pre) vs. geometric mean of pre and post
    • want the transformation that makes the graph flat and random
  • 1/2 of families received a service dog after 3 weeks
  • Suggest longitudinal analysis using 3 daily x 15 weeks, allowing for correlation; only one day per week
  • Correlation structure based on approximate time of measurements in days + fraction of day
  • Model smooth time trend, allowing for separate trend in those randomized to service dog; check for shape change between two groups
  • Easiest-to-interpret method generalized least squares with AR1 continuous-time correlation structure

David Dantzler and Donald Lynch, Cardiovascular Medicine

  • ECMO: what predicts survival to hospital discharge; initiated by cardiac surgeons
  • Collecting patients from last 2 years (N=60 so far)
  • Discussed margin of error of 0.1 in estimating a single probability with n=96
  • Alternate endpoints: LOS, censor on death, i.e. Y=time to successful discharge
  • Or: ordinal outcome Y=1, 2, 3, ... longest LOS, dead = longest LOS + 1; effective sample size almost equal to # subjects
  • Also have Glasgow coma scale at discharge; could factor into ordinal outcome
  • May be possible to use a complex high-information scale to derive a severity of illness-based score that is then used to predict mortality
    • Has reduced many variables to one
  • What to do with patients who died before ECMO was available?


Mitchell Odom, VUMC 3rd year medical student, Department of Neurosurgery.

I am currently helping with a project that requires a survey be employed, and we are creating an original one to send out. I would like to get some expert opinions on the questions that we ask, and to make sure we are honing in on what we're really looking for.
  • CTE - Chronic Traumatic Encephalitis caused by multiple concussions. Survey is designed to ask questions about awareness of CTE among parents of young athletes (junior high and high school). The plan is to distribute the survey using Vanderbilt connections with local high schools.
  • Recommendations:
    • Maximize response rate (by giving parents incentives of some sort)
    • Ensure that the survey is brief
    • Make sure the responses are anonymous
    • Use numbers instead of categories
    • Simplify the language
    • Branch questions
    • Incorporate visual analog scale (instead of categories)
    • Order questions in a logical way

Older Notes

Topic attachments
I Attachment Action Size Date Who Comment
BoxPlotR.RR BoxPlotR.R manage 5.7 K 17 Apr 2006 - 11:44 QingxiaChen  
InforegardingwhatmySPSSfilesays.docdoc InforegardingwhatmySPSSfilesays.doc manage 24.5 K 17 Apr 2006 - 11:44 QingxiaChen  
LOA_condensed_data.sxcsxc LOA_condensed_data.sxc manage 22.1 K 04 Dec 2006 - 09:17 PatrickArbogast Data from Edward Butterworth
Oluwole_Biostat_Clinic.xlsxls Oluwole_Biostat_Clinic.xls manage 46.5 K 25 Aug 2014 - 11:30 SharonPhillips data file for Olalekan Oluwole
StatisticalAnalysisRequest.docdoc StatisticalAnalysisRequest.doc manage 22.5 K 17 Apr 2006 - 10:26 QingxiaChen  
WellsIschemicCollat.pngpng WellsIschemicCollat.png manage 37.0 K 31 Jan 2011 - 13:58 MattShotwell  
WellsIschemicEF.pngpng WellsIschemicEF.png manage 37.4 K 31 Jan 2011 - 13:55 MattShotwell  
analysisEXT analysis manage 3.9 K 11 Feb 2006 - 20:30 QingxiaChen  
biost_clinic_stephanie_vaughn.csvcsv biost_clinic_stephanie_vaughn.csv manage 4.3 K 23 Apr 2007 - 11:37 PatrickArbogast  
biost_clinic_stephanie_vaughn.dtadta biost_clinic_stephanie_vaughn.dta manage 1.7 K 01 May 2007 - 11:12 PatrickArbogast Stata datafile for Stephanie Vaughn
biost_clinic_stephanie_vaughn.loglog biost_clinic_stephanie_vaughn.log manage 8.1 K 01 May 2007 - 11:13 PatrickArbogast Analysis results for Stephanie Vaughn from April 30th clinic
biost_clinic_stephanie_vaughn.xlsxls biost_clinic_stephanie_vaughn.xls manage 25.0 K 23 Apr 2007 - 11:37 PatrickArbogast  
boxplotdata.csvcsv boxplotdata.csv manage 2.7 K 17 Apr 2006 - 10:27 QingxiaChen  
clinicimage.jpgjpg clinicimage.jpg manage 134.8 K 14 Aug 2020 - 10:15 DalePlummer  
clintCarroll.sxcsxc clintCarroll.sxc manage 40.4 K 26 Feb 2006 - 21:30 FrankHarrell Clint Carroll Langerhans Data
clintCarrollabstract.sxwsxw clintCarrollabstract.sxw manage 8.7 K 26 Feb 2006 - 21:27 FrankHarrell Clint Carroll Langerhans Abstract
specificaims.docdoc specificaims.doc manage 25.5 K 13 Feb 2006 - 10:11 ChuanZhou Specific Aims
tang.rdarda tang.rda manage 13.4 K 19 Dec 2009 - 08:42 FrankHarrell Data from Yi Wei Tang processed using R code above
Topic revision: r663 - 16 Nov 2020, HeatherPrigmore

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