ABSTRACT

We consider the problem of estimating the best subgroup and testing for treatment effect in a clinical trial. We define the best subgroup as the subgroup that maximizes a utility function that reflects the trade-off between the subgroup size and the treatment effect. For moderate effect sizes and sample sizes, simpler methods for subgroup estimation worked better than more complex tree-based regression approaches. We propose a three-stage design with a weighted inverse normal combination test to test the hypothesis of no treatment effect across the three stages.

中文翻译：

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具有亚组估计的多阶段自适应富集试验设计

摘要

我们考虑在临床试验中估计最佳亚组和检验治疗效果的问题。我们将最佳子组定义为最大化反映子组大小和治疗效果之间权衡的效用函数的子组。对于中等效应量和样本量，更简单的子组估计方法比更复杂的基于树的回归方法效果更好。我们提出了一个带有加权逆正态组合检验的三阶段设计，以检验三个阶段没有治疗效果的假设。