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:

Provide continuity:

Foster research in clinical departments

◦ develop statistical scientists instead of statistical consultants
◦ develop statistical collaborators not statistical service people or
technicians
◦ fluent in biomedical research areas in order to be effective coinvestigators
◦ available time to collaborate early to increase NIH grant funding
◦ FTE buffer that allows us to be listed on grant applications
◦ 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 19992003
 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:

Usage of robust methods

Usage of powerful methods:

Consideration of the robustness-power or biasvariance tradeoff.
◦ Who is the intended target population?
◦ To what population does your analysis generalize?
◦ Missing data, non-random selection, confounding, effect
modification.
◦ 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.
◦ using analytic methods that get the most out of the data

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/Anesthesiol
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.