🤖 AI Summary
This study addresses the limited external validity of randomized controlled trials, which often enroll participants systematically different from the target population—particularly when effect modifiers are unevenly distributed—rendering the average treatment effect (ATE) inadequate for capturing treatment effect heterogeneity. Within a nested trial framework, the authors propose a novel approach to unbiasedly generalize the conditional average treatment effect (CATE) to the entire eligible population based on pre-specified effect modifiers. Leveraging semiparametric theory and data-adaptive estimation, the method constructs pseudo-outcomes via conditional influence functions and employs local linear kernel regression with cross-fitting to mitigate overfitting. Simulations and an empirical application to the Coronary Artery Surgery Study (CASS) demonstrate that the proposed estimator robustly recovers and enables valid inference on heterogeneous treatment effects in the target population.
📝 Abstract
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).