We design two‐stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision‐theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per‐comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

中文翻译：

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优化两阶段自适应富集和伞形设计中的子组选择

我们设计了两阶段的验证性临床试验，使用适应性来寻找将从新治疗中受益的患者亚组，在两个不相交的亚组中测试治疗效果。我们的提议允许在中期分析中改变试验的各个方面，例如每组的招募概率。我们使用条件错误率方法来实现这些适应，同时保护整体错误率。应用贝叶斯决策理论框架，我们通过最大化考虑子组的人口流行率的效用函数来优化设计参数。我们展示了具有家庭错误率控制（使用封闭测试程序）的传统试验的结果，以及仅控制每次比较类型 1 错误率的伞式试验的结果。