Information about the Department of Biostatistics for New Investigators

If you are interested in attending one of the Biostatistics Clinic sessions please see the Clinics page first.

Biostatistical Services | Presentation

The main goal of the Department of Biostatistics is to increase the quality and quantity of research in the School of Medicine. As a basic science of research methodology, biostatistics can benefit research programs and investigators in many ways including
  • Developing study and experimental designs that maximize the efficiency, increase interpretability and generalizability, and enhance the ethical conduct of research
  • Using modern experimental designs that allow for early termination of experiments when sufficient evidence for effects is demonstrated, or extends experiments when results are equivocal
  • Refining measurements to increase precision and sensitivity
  • Developing grant proposals and increasing their likelihood of being funded
  • Assisting with manuscript writing, increasing the likelihood of acceptance and improving the quality of the result
  • Analyzing data in a powerful, robust, and reproducible fashion
  • Interpreting results using modern statistical graphics for optimum communication to non-statisticians
  • Providing methodologic review and teaching in journal clubs, research conferences, and short courses
  • Participating in K award mentoring
  • Implementing and running secure web-based research data management systems and archiving research databases
The Department currently has 28 faculty and 20 staff M.S. biostatisticians. Details may be found at

Ways of Obtaining Biostatistics Support

  1. Biostatisticians taking part on funded grants and contracts, with funding for appropriate % efforts for PhD and MS biostatisticians
  2. Collaboration plan, with % efforts of biostatisticians funded by collaborating departments with 1:4 matching funds from the Dept. of Biostatistics for faculty biostatisticians
  3. Cancer Center Biostatistics Center core support from the Division of Cancer Biostatistics in the Dept. of Biostatistics
  4. Design, Biostatistics, and Clinical Research Ethics core in the Vanderbilt Institute for Clinical and Translational Research (VICTR-CTSA)
    • Clinical, community, and translational research proposals, upon approval by the VICTR Scientific Review Committee, result in vouchers for biostatistics help and for the following types of studios: design, analysis, manuscript, and studios for fine-tuning funded grants in their startup phase
    • Surgical research that is clinical or translational in nature (i.e., relates to human tissue) uses the VICTR-CTSA mechanism for requesting biostatistics support
  5. Statistics and Methodology Core for the Vanderbilt Kennedy Center
  6. Daily walk-in Biostatistics Clinics
  7. Hourly fees for service
Consulting requests, whether for VICTR-CTSA biostatistics vouchers or otherwise, should be accompanied by this form

Funding Biostatistical Support

Percent efforts for biostatisticians on applications are readily accepted by grant reviewers and granting agencies, so it is common to have 0.15 - 0.5 FTEs allocated to biostatisticians (typically in a 2.5:1 ratio of MS:PhD effort) once a grant is awarded. Developing statistical designs and analysis plans for somewhat complex grants typically requires about 80 hours of biostatistician time. So that we can provide assistance with development of new grants (other than renewals or derivatives of grants that already fund a biostatistician) and data analysis and other services, there are several ways such services can be funded.
  • By enrolling in the percent effort-based collaboration plan
    • This plan allocates MS and PhD biostatistician efforts (typically in the 2.5:1 ratio) to work long-term with research groups, divisions, or departments. This approach makes it easy for the same statisticians to stay involved with a research group and facilitates the building of research teams and planning ahead for grant proposals, manuscripts, and abstract deadlines. It also helps our department plan its recruiting. Groups enrolling in this plan have 20% of the efforts of faculty biostatisticians devoted to the research group paid by the Department of Biostatistics as long as the biostatisticians are doing work that is directly related to research development, teaching, or mentoring.
    • We track time spent working with other departments on this plan so the other groups can make adjustments in percent efforts they fund over time and so that research group leaders know how the resource is being used.
    • An excellent way to fund these efforts is for new faculty to include biostatistical support in their package.
  • By fees for service (draft version)
    • Unlike divisions or research groups enrolling in our collaboration plan in which we provide at least a 20% discount in faculty salaries and fringe benefits covered, the fee for service plan involves the other division or research group paying hourly rates for all salary, fringe benefits, and other allocable direct costs. Fee for service work is managed through the BCC at the current hourly rates.

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Who to Contact

If you are applying for assistance through VICTR (CTSA), apply for CTSA resources by going to

Otherwise, if you would like more information or to arrange a meeting to discuss biostatistical support, contact [[][]], Department of Biostatistics, at or 615-322-2001.

If your research group is already covered by a Biostatistics Collaboration Plan, contact the lead statistician who is responsible for your group. Assignments are listed at CollaborationAssignments.
Related Links

Collaboration Protected Time and Cost-Sharing Model


  • Integrate Biostatistics into research fabric of VU School of Medicine
  • Develop long-term collaborative relationships; develop statistical scientists instead of statistical consultants
  • Provide continuity that will allow biostatisticians to learn enough about biomedical research areas to be effective co-investigators
  • Increase NIH grant funding by maintaining grant development capabilities
  • Help organize research teams that plan in advance and submit better grant proposals that have a higher likelihood of funding
  • Always have someone with available FTE who can be listed on a grant application
  • Be able to respond to RFPs and RFAs with short lead times, and to training grant opportunities
  • Hire new faculty and staff in proportion to anticipated grant funding
  • Foster research in clinical departments, in ways other than just grant development
    • participate in meetings
    • work with fellows and residents
    • improve research methodology skills of faculty
    • help develop new clinical investigators

The Problem

  • It is necessary to fund biostatisticians to work in research development because
    • The research project is more often than not in an area that is unfamiliar to a randomly chosen biostatistician; the biostatistician must invest real time to learn enough about the science to be able to choose the best statistical design and analysis
    • Moderately large grant proposals require more than 40 hours of combined MS + PhD statistician time
    • Any small percent efforts that the Department of Biostatistics would be able to fund to provide grant development support would be quickly overrun by the large number of investigators in need of such support
  • Easy to fund biostatisticians on grant proposals
  • Difficult to hire ahead of funding
  • Difficult to maintain enough unallocated percent efforts to collaborate on new ideas
  • Many PIs see grant proposals as needing only last-minute statistical consulting and may get a different consultant every time
    • Biostatisticians do not have time to do their best work, especially with regard to study design
  • There are ways other than grant proposals that biostatisticians can help foster research in clinical departments
  • VICTR CTSA provides 1.5 FTE biostatistician time for all School of Medicine faculty combined
    • This does not cover basic and animal research and only allows the VICTR Design Biostatistics and Research Ethics core to help with small requests

The Plan

  • Replace consultation (other than for very small projects) with collaboration
  • Support other departments through new long-term collaboration model
  • Department of Biostatistics operating funds provide 1:4 cost matching to other Divisions and Departments for PhD faculty biostatistician time
  • Masters-level biostatistician time is funded by the department/division needed support by a straight % effort payment
  • There is an overhead charge of $8000 per combined PhD+MS FTE per year to pay for IT support, continuing education, travel to scientific meetings, statistical software, hardware, use of ACCRE parallel computing facility, books, and journals
  • Example: Suppose that a Division with 7 active researchers not supported by a biostatistics core facility such as the Cancer Biostatistics Division or the Vanderbilt Kennedy Center needs 1/4 PhD and 1/2 MS biostatistician. That Division would pay
    • 1/2 of salary + fringe for the MS biostatistician
    • 4/5 of 1/4 = 1/5 of the PhD biostatistician's salary + fringe but will have 1/4 of the faculty member's time dedicated to them
    • 3/4 * $8000 = $6000 per year for IT/professional expenses/software/hardware
  • This would provide
    • priority work on grant proposals from this group
    • data analysis for existing non-grant-funded projects
    • study and experimental design for non-grant-funded projects
    • assistance with non-grant-funded manuscripts
    • assistance with journal clubs (methodologic review)
    • assistance with research conference (e.g., data analysis and pre-conference critique of fellows' presentations)
    • teaching short courses in experimental design and analysis methodology for the Division
    • K award mentoring
  • Understandings:
    • The collaboration plan does not cover work funded by grants other than K awards which do not support biostatistician salaries. Biostatistician support should be built into grant budgets during submission.
    • As grant proposals arising from this arrangement are funded, grant funds will supplement rather than replace the collaborative funding arrangement
    • As grants are funded, either new personnel will be hired or identified by the Department of Biostatistics to work on the new projects, or personnel supported by the collaboration arrangement will move to the new projects and new personnel will assume positions funded by the cost-sharing arrangement
    • For groups not requiring full-time collaborators, existing personnel may assume both roles
    • Division or Department head or vice-chair for research will prioritize usage of the resource by her or his faculty
    • Division or Department investigators will provide yearly input to Department of Biostatistics for faculty and staff reviews, regarding quality of collaborations and research by biostatisticians
    • The plan will be used to support abstract preparation on a first-come first-serve basis for fellows, other trainees, and junior faculty as part of their first research project. The intention of the abstract must be to later produce a manuscript for peer review. Normally advance notice of at least one month is required.
  • Option for Divisions/Departments Not in Collaboration Plan
    • VICTR CTSA for small requests for human subjects research; requesting division/department likely to get a different consultant each time without background knowledge of your area
    • Cancer Biostatistics core for cancer research as part of grant proposals
    • Vanderbilt Kennedy Center Statistics & Methodology Core for Kennedy Center investigators
    • Consulting charged by the hour through the Biostatistics Collaboration Center, for both PhD and MS biostatistician time
    • All of these require adequate lead time

How to Take Part in the Program

Tell Frank Harrell about your future needs, which will guide our recruiting of faculty and staff biostatisticians.

Biostatistics Collaborations

The table below includes assignments on our Collaboration Plan as well as other long-term collaborative arrangements, some of them supported by center grants.

Area Department Chair/Division Director Vice Chair For Research Faculty Biostatisticians Staff Biostatisticians/Computer Systems Analysts Report
Anesthesiology (Department of) Warren Sandberg, MD, PhD Edward Sherwood, MD, PhD Matt Shotwell, PhD Yaping Shi, MS  
America's Hernia Society Quality Collaborative Benjamin Poulose, MD   Thomas Stewart, PhD Molly Olson, MS  
Biomedical Ethics and Society (Center for) Keith Meador, MD, ThM, MPH   Frank Harrell, PhD Ahra Kim, MPH
Biomedical Informatics (Department of) DBMI Kevin Johnson, MD Bradley Malin, PhD Cindy Chen, PhD N/A Report
Cardiovascular Medicine (Division of)/DOM Tommy Wang, MD   Frank Harrell, PhD

Shi Huang, PhD

Meng Xu, MS

Emergency Medicine (Department of) Corey Slovis, MD Alan Storrow, MD DandanLiu, PhD Cathy Jenkins, MS Report
Gastroenterology (Division of) Richard Peek, MD Michael Vaezi, MD (Clinical Director) Chris Slaughter, DrPH   Report
Global Health (Institute for) Ed Trevathan, MD, MPH   Bryan Shepherd, PhD Wu Gong, MS Report
Health Policy (Department of) Melinda Buntin, PhD   Laurie Samuels, PhD N/A  
Health Services Research (Center for) Russell Rothman, MD, MPP        
Working with Wes Ely, MD, PhD: Rameela Chandrasekhar, PhD Jennifer Thompson, MPH  
General: Laurie Samuels, PhD N/A  
Hearing & Speech Sciences (Department of) Anne-Marie Tharpe, PhD James Bodfish, PhD     Report
MOON, Sports Medicine Kurt Spindler, MD   Frank Harrell, PhD Sam Nwosu; Charles Dupont, CSA  
Neonatology (Division of) Susan Guttentag, MD   Chris Slaughter, DrPH Jeremy Stephens, CSAII Report
Nephrology (Division of)/DOM Raymond Harris, MD Alp Ikizler, MD Pingsheng Wu, PhD
Thomas Stewart, PhD
Aihua Bian, MPH
Jennifer Morse, MS
Amy Perkins, MS
Neurology (Department of) Dane Chetkovich, MD, PhD Beth Ann Malow, MD Fei Ye, PhD Run Fan, MS  
Ophthalmology (Department of) Paul Sternberg, MD David Calkins, PhD (Vice Chair, Director for Research)   Pengcheng Lu, MS Report
Ophthalmology (Department of) David J. Calkins, PhD   Cindy Chen, PhD To be determined Report
Pathology (Department of) Samuel Santoro, MD, PhD   Bill Dupont, PhD Dale Plummer, BS Report
Pediatrics (Department of) Steven Webber, MBChB, MRCP   Chris Slaughter, DrPH Meng Xu, MS  
Psychiatry (Department of) Stephan Heckers, MD   Frank Harrell, PhD (temp) Ahra Kim, MPH
Rheumatology (Department of) Cecilia Chung, MD, MPH   Bill Dupont, PhD Omair A. Khan, MAS, GStat  
Vaccine Center/Program in Vaccine Science Jim Crowe, MD   Chris Slaughter, DrPH N/A Report
Vanderbilt Memory & Alzheimer's Center Angela Jefferson, PhD   Dandan Liu, PhD Omair A. Khan, MAS, GStat  
Vanderbilt Specialty Pharmacy (VSP) Gerald Buller, PharmD Autumn Bagwell, PharmD Leena Choi, PhD Samuel Nwosu, MS Report
Note: If your department is not participating in a collaboration plan, you may wish to attend one of the Clinics offered through
the Biostatistics Department. If your department is in a collaboration plan, you are always welcome at clinics too.

Potential Collaborations
Area Department Chair/Division Director Vice Chair For Research Faculty Biostatisticians Staff Biostatisticians/Computer Systems Analysts Report
Imaging Science (VU Institute of) John Gore, MD Hakmook Kang, PhD    
Infectious Disease (Division of)/DOM Patty Wright, MD (Current Interim Director), David Aronoff, MD Bryan Shepherd, PhD Cathy Jenkins, MS Report

Start dates of collaboration plans

Department Date
Anesthesiology 18Nov05
Biomedical Ethics 1Jul17
DBMI 14Sep05
ED 29Aug05
Global Health 1Jul09
Hearing and Speech Sciences 1Sep10
Neonatology 9Jan06
Neurology 2Nov05
OBGYN 3Aug04
Opthalmology 17Sep04
Orthopedics 27Apr04
Pathology 5Jun06
Pediatrics 1Nov09
Psychiatry 1Jul17
Vaccine Center 1Sep05
Vanderbilt Specialty Pharmacy 1June17

Benefits of Biostatistics to the Basic Scientist | Clinic

Examples Where Biostatistical Expertise Changes The Results

  • Inappropriate experimental design wasted animals or answered the wrong question
  • Assuming linearity of effects caused in a loss of power or precision
  • Improper transformation of a response variable (e.g., using percent change) caused results to be uninterpretable
  • Apparent interaction between factors (effect modification; synergy) was due to inappropriate transformations of main effects
  • Apparent effect of an interaction of two genes was explained by the main effects of three omitted genes
  • Failure to fully adjust for confounders gave rise to misleading associations with the variable of interest
  • Having no yield from an experiment was predictable beforehand from statistical principles
  • An aggressive analysis of a large number of candidate genes, proteins, or voxels, failing to build a "grain of salt" into the statistical approach, resulted in non-reproducible findings or overstated effects/associations
  • Dichotomizing a continuous measurement resulted in unexplained heterogeneity of response and tremendous loss of power, effectively resulting in discarding 2/3 of the experiment's subjects
  • Use of out-of-date statistical methods resulted in low predictive accuracy and large unexplained variation
  • Removal of "outliers" biased the final results
  • Treating measurements below the limit of detection as if they were actual measurements in the analysis caused results to be arbitrary
  • Misinterpreting "P > 0.05" as demonstrating the absence of an effect

Example Questions Biostatistics Can Answer

  • What does percent change really assume?
  • Why did taking logarithms get rid of high outliers but create low outliers?
  • What is the optimum transformation of my variables?
  • What statistical method can validly deal with values below the detection limit?
  • Do I need more mice or more serial measurements per mouse?
  • What's the best way to analyze multiple measurements per mouse?

Experimental Design

  • Identifying sources of bias: biostatistics can assist in identifying sources of bias that may make results of experiments difficult to interpret, such as
    • litter effects: correlations of responses of animals from the same litter may reduce the effective sample size of the experiment
    • order effects: results may change over time due to subtle animal selection biases, experimenter fatigue, or refinements in measurement techniques
    • experimental condition effects: laboratory conditions (e.g., temperature) that are not constant over the duration of a long experiment may need to be accounted for in the design (through randomization) or in the analysis
    • optimizing measurements: sometimes optimizing measurements (e.g., changing pattern recognition criteria or image analysis parameters) may result in techniques that are too tailored to the current experiment
  • Selecting an experimental design: taking into account the goals and limitations of the experiment to select an optimum design such as a parallel group concurrent control design vs. pre-post vs. crossover design; factorial designs to simultaneously study two or more experimental manipulations; randomized block designs to account for different background experimental conditions; choosing between more animals or more serial measurements per animal. Accounting for carryover effects in crossover designs.
  • Estimating required sample size: computing an adequate sample size based on the experimental design chosen and the inherent between-animal variability of measurements. Sample size can be chosen to achieve a given sensitivity to detect an effect (power) or to achieve a given precision ("margin of error") of final effect estimates. Choosing an adequate sample size will make the experiment informative.
  • Justifying a given sample size: when budgetary contraints alone dictate the sample size, one can compute the power or precision that is likely to result from the experiment. If the estimated precision is too low, the experimenter may decide to save resources for another time.
  • Making optimum use of animals or human specimens: choosing an experimental design that results in sacrificing the minimum number of animals or acquiring the least amount of human blood or biopsies; setting up a factorial design to get two or more experiments out of one group of animals; determining whether control animals from an older experiment can be used for a new experiment or developing a statistical adjustment that may allow such recycling of old data.
  • Developing sequential designs: allowing the ultimate sample size to be a main quantity to be estimated as the study unfolds. Results can be updated as more animals are studied, especially when prior data for estimating an adequate sample size are unavailable. In some cases, experiments may be terminated earlier than planned when results are definitive or further experimentation is deemed futile.
  • Taking number of variables into account: safeguarding against analyzing a large number of variables from a small number of animals.

Data Analysis

  • Choosing robust methods: avoid making difficult-to-test assumptions; using methods that do not assume the raw data to be normally distributed; using methods that are not greatly effected by "outliers" so that one is not tempted to remove such observations from the analysis. Account for censored or truncated data such as measurements below the lower limit of detectability of an assay.
  • Using powerful methods: using analytic methods that get the most out of the data
  • Computing proper P-values and confidence limits: these should take the experimental design into account and use the most accurate probability distributions
  • Proper analysis of serial data: when each animal is measurement multiple times, the responses are correlated. This correlation pattern must be taken into account to achieve accurate P-values and confidence intervals. Ordinary techniques such as two-way analysis of variance are not appropriate in this situation. In the last five years there has been an explosion of statistical techniques for analyzing serial data.
  • Analysis of gene expression data: this requires specialized techniques that often involve special multivariate dimensionality reduction and visualization techniques; attention to various components of error is needed.
  • Dose-response characterizations: estimating entire dose-response curves when possible, to avoid multiple comparison problems that result from running a separate statistical test at each dose.
  • Time-response characterizations: use flexible curve-fitting techniques while preserving statistical properties, to estimate an entire time-response profile when each animal is measured serially. As with dose-response analysis this avoids inflation of type I error that results when differences in experimental groups are tested separately at each time point.
  • Statistical modeling: development of response models that account for multiple variables simultaneously (e.g., dose, time, laboratory conditions, multivariate regulation of cytokines, polymorphisms related to drug response); analysis of covariance taking important sources of animal heterogeneity into account to gain precision and power compared to ordinary unadjusted analyses such as ANOVA. Statistical models can also account for uncontrolled confounding.

Reporting and Graphics

  • Statistical sections: writing statistical sections for peer-reviewed articles, to describe the experimental design and how the data were analyzed.
  • Statistical reports: writing statistical reports and composing tables of summary statistics for investigators
  • Statistical graphics: use many of the state-of-the-art graphical techniques for reporting experimental data that are described in The Elements of Graphing Data by Bill Cleveland, as well as using other high-information high-readability statistical graphics. Translate tables into more easily read graphics.

Data Management, Archiving, and Reproducible Analysis

  • Data management: the biostatistics core can develop computerized data collection instruments with quality control checking; primary data can be quickly converted to analytic files for use by statistical packages. Gene chip data will be managed using relational database software that can efficiently handle very large databases.
  • Data archiving and cataloging: upon request we can archive experimental data in perpetuity in formats that will be accessible even when software changes; data can be cataloged so as to be easily found in the future. This allows control data from previous studies to be searched. Gene chip data will be archived in an efficient storage format.
  • Data security: access to unpublished data can be made secure.
  • Program archiving: the biostatistics core conducts all statistical analyses by developing programs or scripts that can easily be re-run in the future (e.g., on similar new data or on corrected data). These scripts document exactly how analyses were done and allow analyses to be reproducible. These scripts are archived and cataloged.

  • See Hackam DG, Redelmeier DA. Translation of research evidence from animals to humans. JAMA 296:1731-1732; 2006 for a review of basic science literature that documents systemic methodologic shortcomings. In a personal communication on 20Oct06 the authors reported that they found a few more biostatistical problems that could not make it into the JAMA article (for space constraints).
    • none of the articles contained a sample size calculation
    • none of the articles identified a primary outcome measure
    • none of the articles mentioned whether they tested assumptions or did distributional testing (though a few used non-parametric tests)
    • most articles had more than 30 endpoints (but few adjusted for multiplicity, as noted in the article)

Biostatistics Clinic for Basic Scientists: Fridays at noon (D2221 Med Ctr North)

Policies and Procedures for Grant Proposals

Biostatisticians in the Department of Biostatistics participate in grant development in various ways including:

  • assisting with the formation and operation of a proposal development team
  • assisting the investigators in refining study questions and measurement methods
  • developing study and experimental designs
  • writing statistical analysis plans
  • computing precision, power, and sample sizes necessary to achieve a given precision of estimation or a given power

When an investigator claims to need only a sample size calculation, we usually find that their fundamental study design requires improvement. Unless we are provided sufficient time to participate in the study design, we do not work on proposals for which the investigator "only needs a sample size calculation."

Investigators Served

We provide grant development assistance for proposals that are:

  • explicitly covered under the auspices of the CTSA or Cancer Center Biostatistics Core Resources
  • multi-department proposals of major importance to the School that are initiated by the School of Medicine (not by a department of the School)
  • developed by a research group that is participating in our collaboration cost-sharing plan
  • developed by other groups who we will charge by the hour (see ServicesOverview)
  • logical extensions (e.g., competing renewal) of a grant that is already funding a biostatistician who will work on the new proposal
  • research areas of special interest for a PhD faculty biostatistician


Requirement of Advance Notice

Grant proposals require input from a number of experts. Teams assembled at least four months before submission generate the strongest grant proposals. Optimally, team meetings should meet every three weeks to generate at least two major proposal revisions. From the point of view of the biostatisticians, they will then have sufficient time to do their best work and thus, significantly enhance the likelihood of funding.

We usually require two or more months advance notice when asked to provide assistance with proposals. If not, we will provide assistance only if personnel are available and we will charge departments and divisions by the hour for this service. Exceptions will be granted for cases involving resubmissions, renewals, or submissions of proposals that are substantially similar to previous grants in which the biostatisticians have fully participated.

When there is insufficient time for one of our PhD or MS biostatisticians to work with you on a proposal, we will still provide free consultation during our daily Biostatistics Clinics. Although you will not be able to name the consulting statistician as a Key Personnel on the proposal, you will receive valuable advice on how to avoid major design or analysis pitfalls. On rare occasions, when the design is very simple, we can analyze simple pilot datasets or perform sample size calculations.

Use of Biostatisticians' Names on Proposal

We do not allow an investigator to use the name of a PhD or MS biostatistician on a grant or contract proposal unless the biostatistician has fully participated in the development of the proposal from its inception and can vouch for the study design and analysis plan. Donna Bock is grants manager for the Department of Biostatistics; she will work closely with you when preparing your proposal.

Prohibition of unauthorized change of Biostatistician's Names

We do not allow an investigator to change or to remove a biostatistician's name once it is officially listed on a grant proposal unless special permission is obtained from the Chairman of Biostatistics.

Percent Efforts

It is departmental policy that the percent efforts of statisticians on grant proposals reflect their anticipated actual efforts. A faculty member may not be on more than 3 projects with less than 7.5% effort each; the minimum percent effort is generally 5%. Unless the granting agency disallows funding percent efforts, statisticians will not participate on grant applications as internal consultants or be paid a fixed dollar amount or for a fixed number of hours.

We strongly recommend that a grant proposal name both a PhD and an MS biostatistician as members of the research team, with the ratio of MS:PhD percent efforts being about 2.5:1.

Figuring actual percent efforts on grant proposals is more art than science. Generally speaking, without sufficient time invested in the proposal, we cannot make accurate estimates of the future effort required for your project. Factors that necessitate both a PhD and an MS statistician include:

  1. many variables (measurements) per subject or animal
  2. repeated (serial, longitudinal) measurements per animal or subject
  3. a significant number of subjects dropping out before the planned termination of their follow-up
  4. other types of missing data
  5. plans for sequential monitoring of treatment effects or safety with an eye to possible early termination of the study
  6. data and safety monitoring committees
  7. data from other sources where the data must undergo major transformations or cleaning to be analyzable
  8. data from multiple clinical sites
  9. measurements that are only partially observable (e.g., when concentrations may be below the lower limit of detectabilty)

Make sure that in any grant in which you are involved you include the following in the regular budget:
  1. Computer systems analyst support if this support is directly for the aims of the grant, e.g., dealing with large medical databases, web application programming, other data management, programming (especially for simulations). This would be as a % effort to a named IT member.
  2. Travel to professional meetings if (1) you need to go to a medical/basic science meeting that will give you knowledge to collaborate with the investigators or (2) you need to acquire methodologic skills that are directly used in the project for which you need to attend a specific session or short course at a statistical meeting. For (1) funding should be for all costs of the meeting and for (2) funding should be for all or some pro-rated portion of the meeting depending on your % effort on the grant and how directly related the methods are to the grant.
  3. By all means insure that the % effort for the senior + junior biostatistician team are adequate, and the mixture of junior and senior % efforts is appropriate for the expected mixture of Biostat I, II, III, faculty-level tasks involved.


Other Notes

  • Faculty biostatisticians must be designated as Key Personnel
  • Faculty should be designated as a Co-Investigator, when, as a participant in a grant or application he/she contributes substantively to the scientific development or execution of a project or he/she contributes a specified level of time as per NIH guidelines.

Who to contact when considering a new grant proposal

Unless the proposal is a renewal or resubmission (in which case you should contact the biostatisticians currently involved in the project), all requests for assistance on proposals must go to DonnaBock. If you are unsure about whether the proposal development is covered by an existing relationship with our department, contact FrankHarrell.

eRA Commons User Names

Faculty Name eRA Commons
User Name
Ayers, Dan AYERSGD
Byrne, Dan BYRNEDW
Chandrasekhar CHANDRR
Chen, Heidi CHENH5
Chen, Qingxia (Cindy) CHENQ3
Crimin, Kim CRIMINKS
Dupont, William D DUPONTTWD
Federspiel, Charles FEDERSCF
Fonnesbeck, Chris FonnesCJ
Gebretsadik, Tebeb GEBRETT
Greevy, Robert GREEVYRA
Harrell, Frank FHARRELL
Koyama, Tatsuki KOYAMAT
Li, Chun LICHUN3
Li, Ming LIMING2
Saville, Benjamin SAVILLBR
Schildcrout, Jonathan SCHILDJS
Shepherd, Bryan SHEPHEB1
Shintani, Ayumi SHINTAAK
Slaughter, Chris SLAUGHJC
Wang, Lily WANGL8
Wu, Hyiyun (William) WUWILL
Xu, Lei XULEI5
Zhou, Chuan ZHOUC2
Zhu, Yuwei ZHUYU1

To Administrators: For instructions to set up an eRA Commons User Name, click here.

Recommended Reading

See also

Topic revision: r6 - 23 Jan 2017, DalePlummer

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