报告人：张立新 (浙江大学)

邀请人：潘灯

报告时间：2023年3月26日（星期日）11:00-12:00

报告地点：欣苑一栋119报告厅

报告题目：Asymptotic Properties of Covariate-Adaptive Randomization in Clinical Trials

报告摘要：Balancing treatment allocation over influential covariates is an important issue in clinical trials. In literature, a lot of covariate-adaptive randomization (CAR) procedures are proposed for balancing covariates. However, most studies have focused on balancing of discrete covariates. Applications of CAR for balancing continuous covariates remain comparatively rare.  In this talk, we consider a general framework of CAR procedures which can balance general covariate features, such as quadratic and interaction terms which can be discrete, continuous and mixing. We show that the proposed procedures have superior balancing properties; in particular, the convergence rate of imbalance vectors can attain the best rate for discrete covariates, continuous covariates or combinations of both discrete and continuous covariates, and at the same time, the convergence rate of the imbalance of unobserved covariates is , where is the sample size. As an application, the asymptotic properties of the test for the treatment effects are established. The talk is based on works of Hu, Ye and Zhang (2022), Ma, Li, Zhang and Hu (2022), Zhang (2023).

报告人简介：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。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，曾先后主持国家级人才项目、自然科学基金重点项目、面上项目等多项基金，于2008年入选教育部人才支持计划，2018年入选浙江省人才计划，2020年当选Institute of Mathematical Statistics Fellow。