Bayesian Uncertainty Directed Trial Designs

Most Bayesian response-adaptive designs unbalance randomization rates toward the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is different. We discuss Bayesian uncertainty directed designs (BUD), a class of Bayesian designs in which the investigator specifies an information measure tailored to the experiment. All decisions during the trial are selected to optimize the available information at the end of the study. The approach can be applied to several designs, ranging from early stage multi-arm trials to biomarker-driven and multi-endpoint studies. We discuss the asymptotic limit of the patient allocation proportion to treatments, and illustrate the finite-sample operating characteristics of BUD designs through examples, including multi-arm trials, biomarker-stratified trials, and trials with multiple co-primary endpoints. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

多数贝叶斯响应自适应设计会将随机化分配比例向最具前景的受试组倾斜，以期在研究期间增加阳性治疗结局的数量，尽管该试验的核心研究目标并非如此。本文将探讨贝叶斯不确定性导向设计（Bayesian uncertainty directed designs, BUD）——一类由研究者为对应实验量身定制信息测度的贝叶斯试验设计。试验期间的所有决策均以优化研究结束时的可用信息为目标进行遴选。该方法可应用于多种试验设计场景，涵盖早期多臂临床试验、生物标志物驱动型研究以及多终点研究等。本文还将分析患者向各治疗组的分配比例的渐近极限，并通过多臂临床试验、生物标志物分层试验、设置多重共同主要终点的临床试验等实例，阐释贝叶斯不确定性导向设计的有限样本操作特征。本文的补充材料（包含可用于复现本研究的相关材料的标准化说明）可作为在线补充资源获取。