题目：Bayesian Optimal Randomized Designs for Clinical Trials - In a Decision-theoretic Approach

报告人： Professor Yi Cheng (Indiana University South Bend USA)

时间：2017年6月7日 （周三） 下午3：30-- 4：30

地点：致远楼107室

摘要：The presentation includes two parts. The first part is a brief introduction of adaptive designs in clinical trials. The second part presents a specific adaptive design, namely Bayesian optimal randomized design, summarized as follows. Consider a clinical trial with two or more treatments. Assignment of n patients to the treatments is sequential. A design is an allocation policy that specifies which treatment the jth patient will receive, while the information from the previously j-1 treated patients is updated using Bayesian approach and can be used in the decision process. The optimal designs that maximize the overall number of effectively treated patients over the course of the patient horizon N are deterministic. Such designs are appropriately criticized in the context of clinical trials because they are subject to assignment bias. On the other hand, balanced randomized designs may assign an excessive number of patients to a treatment that is performing relatively poorly. We propose a compromise between these two extremes, one that achieves some of the good characteristics of both. We introduce a constrained optimal adaptive design for a fully (or group) sequential randomized clinical trial. We show that, in a trial with the proposed design, fewer patients are assigned to an inferior treatment than when following a balanced design, while preserves its classical characteristics in the inferential statistics.

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