Asymptotic Properties of Covariate-Adaptive Randomization in Clinical Trials for Balancing Covariate

主讲人：张立新 浙江大学特聘教授

时间：2022年11月9日18：00

地点：腾讯会议 402 884 226

举办单位：数理学院

主讲人介绍：张立新，浙江大学求是特聘教授。1995年获复旦大学理学博士学位， 1997年晋升为教授，2001年起先后担任浙江大学统计学研究所副所长、常务副所长、所长，浙江大学数学系副主任、数学科学学院副院长。现任浙江大学数据科学研究中心副主任、中国现场统计研究会常务理事、中国概率统计学会常务理事、浙江省现场统计研究会理事长、中国现场统计研究会试验设计分会副理事长。主要从事概率极限理论、相依数据模型、临床试验自适应随机化设计等领域的研究，发表了学术论文170余篇，先后主持国家自然科学基金面上项目5项、杰出青年基金项目1项、重点项目1项，2018年入选浙江省科技创新领军人才，2020年当选Institute of Mathematical Statistics Fellow。

内容介绍：Balancing treatment allocation over influential covariates is an important issue in clinical trials. In literature, a lot of covariate-adaptive designs are proposed for balancing covariates. In this talk, we consider the asymptotic properties of the covariate-adaptive designs. The asymptotic type I error and asymptotic power of hypothesis testing to compare the treatment effects under covariate-adaptive randomization procedures are considered. It is shown that the traditional test has not precise type I error and will lose power if the covariates are not balanced well. Basing on the asymptotic properties of a wide class of covariate-adaptive randomization procedures which can balance general covariate features, the asymptotically efficient covariate-adaptive designs are introduced so that the loss of power is asymptotically ignorable. The talk is based on works of Ma, Hu and Zhang (2015), Hu and Zhang (2013), Hu, Ye and Zhang (2022+), Ma, Li, Zhang and Hu (2022+).