报告题目:Asymptotic Properties of Covariate-Adaptive Randomization in Clinical Trials
报告时间:2023-03-25 11:00-12:00
报告人:张立新 教授 浙江大学
报告地点:理学院东北楼四楼报告厅
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 O_p
(1) 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 O_p
(√n),where n 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).