Jonathan Schildcrout, Ph.D.Assistant Professor

Department of BiostatisticsDepartment of Anesthesiology

Vanderbilt has grown to the point where Biostatistics could be 100 percent NIH funded on grants.

Problems:◦ If we’re fully funded

no time to work on developing new proposals / collaborations

cannot be listed on a new NIH proposal challenges with hiring moderately large clinical grant proposals often

require 50+ hours of statistician time to prepare

Integrate Biostatistics into research fabric of VU SOM Create long-term collaborative relationships:

◦ develop statistical scientists instead of statistical consultants◦ develop statistical collaborators not statistical service people or

technicians Provide continuity:

◦ fluent in biomedical research areas in order to be effective co-investigators

◦ available time to collaborate early to increase NIH grant funding◦ FTE buffer that allows us to be listed on grant applications

Foster research in clinical departments◦ participate in all phases of departmentally sponsored research◦ improve research methodology skills of faculty through

collaboration and education◦ help develop new clinical investigators, fellows, and residents

Jonathan Schildcrout, PhD◦ Education

MS: Biostatistics, University of North Carolina, 1996. PhD: Biostatistics, University of Washington, 2004.

◦ Experience Clinical trials statistician: Duke University, 1996-1998 Northwest Center for Particulate Matter and Health 1999-

2003 Assistant professor, Vanderbilt, 2004

Longitudinal data analysis and study designs for longitudinal data

Methods for early detection of drug safety Medication related adverse event using EMR eMERGE project use EMR to define phenotype in order to

conduct GWAS and PheWAS

◦ Education MS: Biostatistics University of Washington 2006.

◦ Experience National Alzheimer’s Coordinating Center, University

of Washington, 2007-2008 Biostatistician II, Vanderbilt University, June 2008-

Large randomized clinical trials Anesthesiology collaboration

Education◦ MS: Applied Mathematics, University of Toledo, 2006◦ MS: Biostatistics, University of Iowa, 2008.

Experience◦ Biostatistician II, Vanderbilt University, 2008-

Medication related adverse events using EMR Department of Neurology Anesthesiology collaboration plan.

Experimental design for non-NIH grant funded projects

Data analysis and reproducible reports Manuscript writing: Methods, Statistical Analysis

and Results sections Grant proposals: development analysis plans

and write statistical methods sections Education: study design and analysis

methodology. Overall: key participants in all aspects of the

departmental research enterprise

Defining the study question◦ Independent variables:

predictor of interest confounders

◦ Dependent variable response

Making optimum design choices: ◦ Maximizing information content per participant

recruited or per dollar spent ◦ Design efficiency / minimize variance or

uncertainty

Sample size / power estimation: ◦ Sample size can be chosen to achieve

sensitivity to detect an effect (power) precision ("margin of error") of final effect estimates.

◦ Choosing an adequate sample size will make the experiment informative. underpowered studies are completely uninformative

and do more harm than good (waste money and lead others in the wrong direction).

Consideration of sources of bias: ◦ Who is the intended target population?◦ To what population does your analysis generalize?◦ Missing data, non-random selection, confounding,

effect modification. Usage of robust methods

◦ avoid making difficult-to-test assumptions (e.g., normality)

◦ Less worry about the impact of "outliers." so that no one is tempted to remove such observations.

Usage of powerful methods: ◦ using analytic methods that get the most out of the

data Consideration of the robustness-power or bias-

variance tradeoff.

Program archiving ◦ We write programs that can be re-run in the future and can

be examined to see exactly how the results came about. Statistical reports

◦ A comprehensive analysis and interpretation of study results for investigators

Statistical graphics◦ Graphical techniques for reporting experimental data ◦ High-information high-readability graphics

Statistical and all other sections of peer-reviewed articles◦ Description of the experimental design and data analysis.◦ Assistance with interpreting study results and specifically

with Results sections.

1) Identification of the topic, initial meetings and discussions with collaborators / mentors regarding relevance, goals, and feasibility.

2) Contact Damon Michaels about the project to get things rolling.

3) Complete a protocol: A detailed description of the study

Likely evolve as the project is planned Deliberately resembles the IRB submission form.

4) Organize an informal studio-like session (1.5 hours). • In attendence (all having received the protocol in advance)• an independent senior investigator / mentor / AREC member, two biostatisticians,

and Damon Michaels • To include• 15-20 minute presentation: Background and relevance, specific aims (well-defined

scientific questions), data sources, forseeable challenges and concerns• A discussion that refines the proposal and study goals, and that puts the

investigator on the right path.

5) Follow-up meeting with Biostatistics to discuss feasibility: plan sample size calculations

6) Biostatistics will conduct power/sample size calculations

7) Develop data collection tools / case report forms (StarBRITE has examples) while keeping Damon Michaels and Biostatistics integrally involved.

8) Obtain IRB and other appropriate approval

http://biostat.mc.vanderbilt.edu/AnesthesiologyCollaboration

Advantages over tables

Randomized clinical trials A number of survey studies Retrospective cohort studies Longitudinal and interventional cohort study Power and sample size calculations for a

number of studies

We cannot drop other work to handle preventable emergencies.

Plan early and include us early. Do not rush planning phases of studies All projects should result in a publication.

◦ Abstracts are only interim and should reflect what the manuscript will ultimately address.

Data management ◦ develop computerized data collection instruments

with quality control checking◦ convert primary data to analytic files for use by

statistical packages in an automated fashion.