Jonathan Schildcrout, Ph.D. Assistant Professor Department of Biostatistics Department 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,  PhD: Biostatistics, University of Washington, ◦ Experience  Clinical trials statistician: Duke University,  Northwest Center for Particulate Matter and Health  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 ◦ Experience  National Alzheimer’s Coordinating Center, University of Washington,  Biostatistician II, Vanderbilt University, June  Large randomized clinical trials  Anesthesiology collaboration

 Education ◦ MS: Applied Mathematics, University of Toledo, 2006 ◦ MS: Biostatistics, University of Iowa,  Experience ◦ Biostatistician II, Vanderbilt University,  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 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

 ogyCollaboration

 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.