When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation‐based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment.

中文翻译：

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适应性浓缩试验：有什么好处？

计划进行III期临床试验时，假设某些患者对新疗法的反应特别好。适应性浓缩设计利用过渡期数据为其余试验选择目标人群，既可以继续整个人群，也可以将招募对象限制为一部分患者。我们定义了一个多重测试程序，该程序可以对家庭错误率进行严格控制，同时允许自适应采样程序。我们得出了贝叶斯最优规则，用于确定在中期分析之后是否将招聘限制在子集中，并提出了一种有效的算法来促进基于仿真的优化，从而可以在各种问题公式中构造贝叶斯最优规则。