Am J Epidemiol. 2021 Jun 1;190(6):1088-1100. doi: 10.1093/aje/kwaa235.
Here we describe methods for assessing heterogeneity of treatment effects over prespecified subgroups in observational studies, using outcome-model-based (g-formula), inverse probability weighting, doubly robust, and matching estimators of subgroup-specific potential outcome means, conditional average treatment effects, and measures of heterogeneity of treatment effects. We compare the finite-sample performance of different estimators in simulation studies where we vary the total sample size, the relative frequency of each subgroup, the magnitude of treatment effect in each subgroup, and the distribution of baseline covariates, for both continuous and binary outcomes. We find that the estimators' bias and variance vary substantially in finite samples, even when there is no unobserved confounding and no model misspecification. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (August 1975-December 1996) to compare the effect of surgery plus medical therapy with that of medical therapy alone for chronic coronary artery disease in subgroups defined by previous myocardial infarction or left ventricular ejection fraction.
在这里，我们描述了使用基于结果模型（g 公式）、逆概率加权、双重稳健和匹配的亚组特异性潜在结果均值、条件平均处理效应和处理效果异质性度量的观测研究中针对特定亚组的治疗效果异质性的评估方法。我们在模拟研究中比较了不同估计器的有限样本性能，其中我们改变了总样本量、每个亚组的相对频率、每个亚组的治疗效果大小以及基线协变量的分布，包括连续和二进制结果。我们发现，即使在没有未观察到的混杂和没有模型误设定的情况下，估计器的偏差和方差在有限样本中也会发生很大变化。作为说明，我们将这些方法应用于冠状动脉手术研究（1975 年 8 月至 1996 年 12 月）的数据，以比较手术加药物治疗与单纯药物治疗对先前心肌梗死或左心室射血分数定义的慢性冠状动脉疾病亚组的影响。
