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Inference on Selected Subgroups in Clinical Trials

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  • Xinzhou Guo
  • Xuming He

Abstract

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. Supplementary materials for this article are available online.

Suggested Citation

  • Xinzhou Guo & Xuming He, 2021. "Inference on Selected Subgroups in Clinical Trials," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1498-1506, July.
    • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:535:p:1498-1506
    DOI: 10.1080/01621459.2020.1740096
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    Cited by:

    1. Dominic Coey & Kenneth Hung, 2022. "Empirical Bayes Selection for Value Maximization," Papers 2210.03905, arXiv.org, revised Jan 2023.
    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.

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