Estimating Subgroup Effects in Generalizability and Transportability Analyses.
Estimating Subgroup Effects in Generalizability and Transportability Analyses.
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估计普遍性和可移植性分析中的子组效应。
DOI：
10.1093/aje/kwac036
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发表时间：
2024
影响因子：
5
通讯作者：
Dahabreh,IssaJ
中科院分区：
文献类型：
--
作者：
Robertson,SarahE;Steingrimsson,JonA;Joyce,NinaR;Stuart,ElizabethA;Dahabreh,IssaJ
将随机试验的推论推广或转移到目标人群的方法涉及到一个大的协变量集，该协变量集足以使随机组和非随机组互换。然而，决策者往往有兴趣在目标人群的亚组中检查治疗效果，这些亚组仅根据少数离散协变量定义。在这里，我们提出的方法估计亚组特定的潜在结果的手段和平均治疗效果的推广和可移植性分析，使用结果模型为基础的（g公式），加权和增强加权估计。我们考虑在目标人群及其非随机化亚组中估计亚组特异性平均治疗效应，并提供适用于嵌套和非嵌套试验设计的方法。作为一个例子，我们应用的方法，从冠状动脉手术研究（北美，1975年至1996年）的数据，比较手术加药物治疗与药物治疗单独为慢性冠状动脉疾病的心肌梗死史定义的亚组的影响。
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 nonrandomized 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 nonrandomized subset, and we 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 (North America, 1975–1996) 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.