Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals

TL;DR

This paper proposes a method to estimate how treatment effects vary across individuals in a target population, improving personalized insights from nested randomized trials.

Contribution

It introduces a semiparametric, flexible approach combining influence functions and local regression to estimate conditional treatment effects with valid inference.

Findings

Method performs well in simulations

Applied to Coronary Artery Surgery Study data

Provides individualized treatment effect estimates

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).