Statistical Methods for Estimating Causal Risk Differences (PRISM)

Project Title Statistical Methods for Estimating Causal Risk Differences (PRISM)
Date Posted
Wednesday, March 7, 2012
Status
Complete
Deliverables
Description

Project to develop a new method for the distributed data setting to control for multiple confounders in the concurrent control design with a single time exposure, e.g., vaccine, assessing elevated rates of rare acute outcomes when the quantity of interest is a risk difference. The method proposed is applicable to both a single-time analysis and a group sequential analysis design.

The programming code provided here has not been formally audited in accordance with the Mini-Sentinel Standard Operating Procedure for Quality Control of SAS Programs. It is being provided in the spirit of supporting the exploration and development of novel scientific and statistical methods using observational healthcare data.

Workgroup Leader(s)

Andrea J. Cook PhD; Biostatistics Unit, Group Health Research Institute and Department of Biostatistics, University of Washington, Seattle, WA

Workgroup Members

Robert D. Wellman MS; Biostatistics Unit, Group Health Research Institute, Seattle, WA

Tracey L. Marsh MS; Department of Biostatistics, University of Washington and Group Health Research Institute, Seattle, WA

Ram C. Tiwari PhD; Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, MD

Jennifer C. Nelson PhD; Biostatistics Unit, Group Health Research Institute and Department of Biostatistics, University of Washington, Seattle, WA

Michael D. Nguyen MD; Estelle Russek-Cohen PhD; Zhen Jiang PhD; Center for Biologics Evaluation and Research, FDA, Silver Spring, MD