On the anesthesiology – biostatistics collaboration plan
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Transcript On the anesthesiology – biostatistics collaboration plan
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.