对全面亚组治疗效应图中同质性的证据评估
Assessment of evidence against homogeneity in exhaustive subgroup treatment effect plots
AI总结：
本文提出了一种计算高效的方法，用于在全面亚组治疗效应图中生成同质性区域，通过双重稳健学习器量化异质性，并通过模拟验证其有效性。
摘要：
全面亚组治疗效应图是通过将感兴趣的亚组治疗效应与亚组样本量进行显示，从而提供临床试验中观察到的治疗效应异质性的有用概述。然而，如同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影响。为了促进更具解释力的探索性评估，本文介绍了一种计算效率高的方法，用于在全面亚组治疗效应图中生成同质性区域。利用双重稳健（DR）学习器，伪结果用于估计亚组效应并推导参考分布，量化在同质效应模型下观察到的异质性有多令人惊讶。推导了同质性区域的显式公式，并比较了不同计算临界值的方法。该方法通过心血管试验进行说明，并通过模拟评估，显示出校准良好的推断和比使用简单组均值差的标准方法有改进的表现。
英文摘要：
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.
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