题目：Toward better practice of covariate adjustment in randomized clinical trials

汇报人： Jun Shao

会议时间：2026年1月6日(周二)  9：30

地点：综合楼644会议室

报告人简介：

 Dr. Jun Shao holds a PhD in statistics from the University of Wisconsin-Madison. He is a Professor of Statistics at the University of Wisconsin-Madison. He is a Fellow of American Statistical Association, and a Fellow of Institute of Mathematical Statistics. He is currently the editor-in-chief of Statistical Theory and Related Fields. He has published over 220 research articles. His research interests include variable selection, sample surveys, missing data problems, covariate. 

报告摘要：In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. We present three considerations for better practice when model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (1) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (2) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (3) robust standard error:  variance estimation should be robust against model misspecification and heteroscedasticity. To achieve these, we recommend an analysis of heterogeneous covariance working model including all covariates utilized in randomization. Our conclusions are based on  an asymptotic theory that provides a clear picture of how covariate-adaptive randomization and covariate adjustment alter statistical efficiency.