Methods for extending -- generalizing or transporting -- inferences from a randomized trial to a target population involve conditioning on a large set of covariates that is sufficient for rendering the randomized and non-randomized groups exchangeable. Yet, decision-makers are often interested in examining treatment effects in subgroups of the target population defined in terms of only a few discrete covariates. Here, we propose methods for estimating subgroup-specific potential outcome means and average treatment effects in generalizability and transportability analyses, using outcome model-based (g-formula), weighting, and augmented weighting estimators. We consider estimating subgroup-specific average treatment effects in the target population and its non-randomized subset, and provide methods that are appropriate both for nested and non-nested trial designs. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study to compare the effect of surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease in subgroups defined by history of myocardial infarction.

翻译：扩大 -- -- 一般化或运输 -- -- 从随机试验到目标人口的推论方法 -- -- 从随机试验到目标人口的推论 -- -- 需要以大量共变法为条件,这些共变法足以使随机和非随机群体能够互换,然而,决策者往往有兴趣审查目标人群分组的治疗效果,这些分组仅以少数离散的共变法界定。这里,我们建议了估计各分组潜在潜在结果手段和平均治疗效果的方法,以及一般可变性和可迁移性分析中的平均治疗效果的方法,我们采用基于结果模型(g-公式)、加权和增加重量的估测器。我们考虑估计各分组在目标人群及其非随机化子群中的平均治疗效果,并提供适合巢状和非惯用试验设计的方法。举例来说,我们采用科诺氏动脉外科研究数据的方法,以比较外科外加医疗疗法的影响,以及在由心肌死亡史界定的分组中仅用于慢性动脉病的单体外科疗法。