A common goal in comparative effectiveness research is to estimate treatment effects on prespecified subpopulations of patients. Though widely used in medical research, causal inference methods for such subgroup analysis (SGA) remain underdeveloped, particularly in observational studies. In this article, we develop a suite of analytical methods and visualization tools for causal SGA. First, we introduce the estimand of subgroup weighted average treatment effect and provide the corresponding propensity score weighting estimator. We show that balancing covariates within a subgroup bounds the bias of the estimator of subgroup causal effects. Second, we propose to use the overlap weighting (OW) method to achieve exact balance within subgroups. We further propose a method that combines OW and LASSO, to balance the bias-variance tradeoff in SGA. Finally, we design a new diagnostic graph—the Connect-S plot—for visualizing the subgroup covariate balance. Extensive simulation studies are presented to compare the proposed method with several existing methods. We apply the proposed methods to the patient-centered results for uterine fibroids (COMPARE-UF) registry data to evaluate alternative management options for uterine fibroids for relief of symptoms and quality of life.

中文翻译：

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因果亚组分析的倾向得分加权

比较有效性研究的一个共同目标是估计对预先指定的患者亚群的治疗效果。尽管在医学研究中广泛使用，但此类亚组分析 (SGA) 的因果推理方法仍然不发达，特别是在观察性研究中。在本文中，我们为因果 SGA 开发了一套分析方法和可视化工具。首先，我们介绍了亚组加权平均治疗效果的估计量，并提供了相应的倾向得分加权估计量。我们表明，平衡子组内的协变量限制了子组因果效应估计量的偏差。其次，我们建议使用重叠加权（OW）方法来实现子组内的精确平衡。我们进一步提出了一种结合 OW 和 LASSO 的方法，以平衡 SGA 中的偏差-方差权衡。最后，我们设计了一个新的诊断图——Connect-S 图——用于可视化子组协变量平衡。提出了广泛的模拟研究，以将所提出的方法与几种现有方法进行比较。我们将所提出的方法应用于以患者为中心的子宫肌瘤结果 (COMPARE-UF) 登记数据，以评估用于缓解症状和提高生活质量的子宫肌瘤替代管理方案。