【报告摘要】Randomized clinical trials (RCTs) are widely considered the gold standard for comparing an experimental treatment with a control treatment. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate-dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial. Our optimality results translate into optimal designs that improve upon standard practice.

【报告人简介】张维，中国科学院数学与系统科学研究院副研究员。2016博士毕业于中国科学院数学与系统科学研究院。研究方向包括临床试验、诊断医学、分组检测及遗传关联分析中的统计理论、方法和应用。发表SCI论文40余篇，ESI高被引论文1篇，曾获国家青年人才计划和中科院青年人才计划项目资助。