Abstract:

In clinical trials and other comparative studies, covariate balance is crucial for credible and efficient assessment of treatment effects. Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring. In the literature, most studies have focused on balancing of discrete covariates. Applications of CAR with continuous covariates remain rare, especially when the interest goes beyond balancing only the first moment. In this paper, we propose a family of CAR procedures that can balance general covariate features, such as quadratic and interaction terms. Our framework not only unifies many existing methods, but also introduces a much broader class of new and useful CAR procedures. We show that the proposed procedures have superior balancing properties; in particular, the convergence rate of imbalance vectors is $O_P(n^{\epsilon})$ for any $\epsilon>0$ if all of the moments are finite for the covariate features, relative to $O_P(\sqrt n)$ under complete randomization, where $n$ is the sample size. Both the resulting convergence rate and its proof are novel. These favorable balancing properties lead to increased precision of treatment effect estimation in the presence of nonlinear covariate effects. The framework is applied to balance covariate means and covariance matrices simultaneously. Simulation and empirical studies demonstrate the excellent and robust performance of the proposed procedures.

Speaker:

马维，中国人民大学统计与大数据研究院长聘副教授、博士生导师，国家级青年人才项目获得者。研究兴趣包括自适应设计、临床试验设计与分析、生物统计、健康医疗大数据等。在Journal of the American Statistical Association、Biometrika、Biometrics等期刊上发表多篇学术论文。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。担任中国现场统计研究会试验设计分会理事、因果推断分会理事。