Exhaustive subgroup treatment effect plots are constructed by displaying all subgroup treatment effects of interest against subgroup sample size, providing a useful overview of the observed treatment effect heterogeneity in a clinical trial. As in any exploratory subgroup analysis, however, the observed estimates suffer from small sample sizes and multiplicity issues. To facilitate more interpretable exploratory assessments, this paper introduces a computationally efficient method to generate homogeneity regions within exhaustive subgroup treatment effect plots. Using the Doubly Robust (DR) learner, pseudo-outcomes are used to estimate subgroup effects and derive reference distributions, quantifying how surprising observed heterogeneity is under a homogeneous effects model. Explicit formulas are derived for the homogeneity region and different methods for calculation of the critical values are compared. The method is illustrated with a cardiovascular trial and evaluated via simulation, showing well-calibrated inference and improved performance over standard approaches using simple differences of observed group means.

翻译：穷举亚组治疗效果图通过展示所有关注亚组的治疗效果与亚组样本量的关系，为临床试验中观察到的治疗效果异质性提供了有用的概览。然而，与任何探索性亚组分析一样，观察到的估计值受到小样本量和多重性问题的困扰。为了促进更具可解释性的探索性评估，本文提出了一种计算高效的方法，用于在穷举亚组治疗效果图中生成同质性区域。该方法使用双重稳健（DR）学习器，利用伪结果估计亚组效应并推导参考分布，从而量化在同质效应模型下观察到的异质性的显著程度。文中推导了同质性区域的显式公式，并比较了计算临界值的不同方法。该方法通过一项心血管试验进行说明，并通过模拟评估，结果表明其推断具有良好的校准性，且相较于使用观察组均值简单差异的标准方法具有更优的性能。