Speaker: Kelly Van Lancker, Postdoctoral Fellow, Biostatistics Department, BSPH
Abstract: In clinical trials, there is potential to improve precision and reduce the required sample size by appropriately adjusting for baseline variables in the statistical analysis. This is called covariate adjustment. Despite recommendations by the U.S. Food and Drug Administration and the European Medicines Agency in favor of covariate adjustment, it remains underutilized leading to inefficient trials. We address two obstacles that make it challenging to use covariate adjustment. A first obstacle is the incompatibility of many covariate adjusted estimators with commonly used stopping boundaries in group sequential designs (GSDs). A second obstacle is the uncertainty at the design stage about how much precision gain will result from covariate adjustment; an incorrect projection of a covariate’s prognostic value risks an over- or underpowered trial.
Zoom: https://jh.zoom.us/j/96582429515?pwd=SXJlUzI0dVRmT1dJR25EZ0YzQisyUT09
