时 间：2026年10月23日 11:00 -12:00
地 点：普陀校区理科大楼A1314
报告人：刘阳 中国人民大学准聘副教授
主持人：王亚平 华东师范大学统计学院
摘 要：
Recent clinical studies increasingly involve a long factor with many levels, e.g., investigation sites, resulting in a large number of strata that must be accounted for either through design or subsequent analysis. This complication has raised concerns by the U.S. Food and Drug Administration (FDA 2023) regarding the adequacy of standard statistical methods, whose performance may deteriorate, and their properties become unclear when the number of strata is relatively large. In this work, we offer a first-time rigorous solution by employing mixed-effect models in covariate- adaptive randomized experiments. We show that the mixed-effect estimate achieves lower variance in treatment effect estimation than its fixed-effect counterpart in the presence of the long factor with many levels. This variance reduction is attributable to marginal imbalances induced by the randomization procedure, suggesting that designs promoting finer covariate balance lead to more efficient inference and increased statistical power. Furthermore, we demonstrate that, as the sample size grows, the mixed- and fixed-effect estimators become asymptotically equivalent. Our theoretical findings are validated through simulations and a clinical trial case study, and provide new insights on the design and analysis of clinical trials in the presence of a large number of strata.
报告人简介：
刘阳，2014年本科毕业于人民大学统计学院， 2022年博士毕业于乔治华盛顿大学统计学专业。毕业后加入中国人民大学统计与大数据研究院任助理教授、博士生导师、准聘副教授。研究兴趣包括：复杂场景下的自适应试验设计，临床试验设计与分析、业界A/B试验设计。