学术报告：Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective

报告时间：2023年11月15日（星期三）下午15:00-16:00

报告地点：沙河校区，学院1号楼102会议室

报告人：刘汉中，清华大学，副教授

报告摘要：Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this paper, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For uneqxual allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.

报告人简介：清华大学统计学研究中心副教授，北京大学统计学博士，美国加州大学伯克利分校博士后。主要研究兴趣包括高维统计推断、机器学习和因果推断。研究成果发表于PNAS、JASA、Biometrika等统计学顶级期刊。主持国家自然科学基金项目2项，参与国家重点研发计划1项。担任全国工业统计学教学研究会青年统计学家协会理事、北京应用统计学会理事、中国现场统计研究会计算统计分会副秘书长。