Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
J Biopharm Stat. 2020 Nov 1;30(6):1038-1049. doi: 10.1080/10543406.2020.1832109. Epub 2020 Oct 18.
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.
我们考虑了在临床试验中估计最佳亚组和检验治疗效果的问题。我们将最佳亚组定义为最大化反映亚组大小和治疗效果之间权衡的效用函数的亚组。对于中等效应大小和样本大小，较简单的亚组估计方法比更复杂的基于树的回归方法效果更好。我们提出了一个三阶段设计，使用加权逆正态组合检验来检验三个阶段内无治疗效果的假设。
