Statistical Inference of Covariate-Adjusted Randomized Experiment

Thursday, November 8, 2018 - 2:30pm - 3:00pm
Lind 305
Feifang Hu (George Washington University)
Covariate-adjusted randomization procedure is frequently used in comparative studies (such as clinical trials for precision medicine) to increase the covariate balance across treatment groups. However, as the randomization inevitably uses the covariate information when forming balanced treatment groups, the validity of classical statistical methods following such randomization is often unclear.In this talk, we discuss the theoretical properties of statistical methods based on general covariate-adjusted randomization under the linear model framework. More importantly, we explicitly unveil the relationship between covariate-adjusted and inference properties by deriving the asymptotic representations of the corresponding estimators. Based on the theoretical results, we then propose a new approach to obtain valid and more powerful tests. These results open a door to understand and analyze experiments based on covariate-adjusted randomization. Simulation studies provide further evidence of the advantages of the proposed framework and theoretical results.