Randomized controlled trials often enroll participants whose characteristics differ from those of a target population, which can limit the generalizability of the estimated treatment effects when effect modifiers differ across populations. While existing generalizability methods primarily focus on estimating the average treatment effect (ATE) in the target population, such summaries may obscure important heterogeneity that is relevant for clinical and policy decision-making. In this work, we illustrate an approach for estimating the conditional average treatment effect (CATE) in a target population of trial-eligible individuals as a function of prespecified effect modifiers within a nested trial setting. Our approach combines semiparametric theory with flexible estimation: we first estimate nuisance functions using data-adaptive methods and construct pseudo-outcomes from conditional influence functions, then estimate the CATE function via local linear (kernel) regression. Sample splitting and cross-fitting are used to reduce overfitting bias and ensure asymptotic valid inference. Finite-sample performance is assessed via simulations and illustrated in the Coronary Artery Surgery Study (CASS).

翻译：随机对照试验常纳入特征与目标人群存在差异的受试者，当效应修饰因子在不同人群中表现不同时，这可能会限制估计处理效应的可推广性。尽管现有推广方法主要聚焦于估计目标人群的平均处理效应（ATE），但此类汇总统计可能掩盖与临床及政策决策相关的显著异质性。本研究提出一种方法，用于在嵌套试验情境下，基于预设的效应修饰因子，估计试验合格目标人群的条件平均处理效应（CATE）。该方法结合半参数理论与灵活估计：首先利用数据自适应方法估计干扰参数，通过条件影响函数构造伪结局，继而采用局部线性（核）回归估计CATE函数。通过样本分割与交叉拟合降低过拟合偏差并保证渐近有效性推断。通过模拟验证有限样本性能，并基于冠状动脉手术研究（CASS）进行实际应用展示。