Inference on Selected Subgroups in Clinical Trials
Inference on Selected Subgroups in Clinical Trials
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DOI：
10.1080/01621459.2020.1740096
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发表时间：
2020-04-17
影响因子：
3.7
通讯作者：
He, Xuming
中科院分区：
文献类型：
--
作者：
Guo, Xinzhou;He, Xuming
当现有的临床试验数据表明一个有希望的亚组时，我们必须解决所选择的亚组到底有多好的问题。应用于所选亚组的通常统计推断（假设亚组是独立于数据选择的）可能导致对所选亚组的过度乐观评价。在这篇文章中，我们解决了选择偏差的问题，并开发了一个去偏自助推断程序的最佳选择的亚组效应。建议的推理过程是无模型的，易于计算，渐近尖锐。我们证明了我们提出的方法的优点，通过重新分析的MONET 1试验，并表明，如何选择的子组事后应发挥重要作用，在任何统计分析。
When existing clinical trial data suggest a promising subgroup, we must address the question of how good the selected subgroup really is. The usual statistical inference applied to the selected subgroup, assuming that the subgroup is chosen independent of the data, may lead to an overly optimistic evaluation of the selected subgroup. In this article, we address the issue of selection bias and develop a de-biasing bootstrap inference procedure for the best selected subgroup effect. The proposed inference procedure is model-free, easy to compute, and asymptotically sharp. We demonstrate the merit of our proposed method by reanalyzing the MONET1 trial and show that how the subgroup is selected post hoc should play an important role in any statistical analysis.for this article are available online.