title: Adaptive covariate adjustment in clinical trials.
abstract: In analysis of covariance (ANCOVA), as a result of covariate adjustment, the estimated mean difference between the two comparative treatment groups may have a better precision than the unadjusted estimate. The extent of improvement of precision depends on the correlation between the outcome variable and the covariate selected for adjustment. Therefore, for this purpose, it is desirable to apply a proper transformation to this covariate so that the transformed covariate has a stronger correlation with the outcome variable. The best predictor from the covariate for the outcome variable is the conditional expectation of the outcome variable given the covariate. Thus, a viable strategy is using regression modeling approach to search for a statistical model to well approximate the conditional expectation based on external and/or current trial data. We propose an adaptive strategy to achieve this goal if the current data are needed to help the search.
authors: Division of Biometrics II, OB/OPaSS/CDER, FDA, HFD-715, Rockville, Maryland, USA. wangs@cder.fda.gov
authors: Sue-Jane
authors: SJ
authors: Wang
authors: H M James
authors: HM
authors: Hung
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic; D012107:Research Design