Efficient transport and generalization of survival treatment effects

2026-09-16

AI Summary

本文解决了随机对照试验结果外推至更广泛目标人群的问题，通过开发非参数偏差校正机器学习估计器，在离散时间下传输和泛化因果生存治疗效果差异。

Abstract

Randomized controlled trials provide internally valid estimates of treatment effects, but their results may not directly apply to broader target populations due to differences in baseline covariate distributions, adherence to treatment or variations in outcome mechanisms. Under standard transport and time-to-event identifiability assumptions, we develop nonparametric, debiased machine learning estimators for transporting and generalizing causal survival treatment effect differences from a source population to a target population in discrete time. We derive the efficient influence functions for the transport and generalization survival difference estimands and propose cross-fitted one-step estimators that are doubly robust and achieve semiparametric efficiency bounds under weak regularity conditions. We further introduce estimators that exploit known effect modifier subsets through an additive parameterization of the survival function, reducing the dimensionality of the reweighting and yielding smaller or equal asymptotic variance. We establish asymptotic normality, double robustness, and rates of convergence for all proposed estimators. Finite-sample properties are illustrated through Monte Carlo simulations under flexible and misspecified nuisance estimation scenarios. We apply the methods to data from the Women's Health Initiative to estimate the effect of hormone therapy on coronary heart disease across trial and observational populations.

Problem

• transport
• generalization
• survival treatment effect
• target population
• randomized controlled trials

Innovation

• nonparametric debiased machine learning
• efficient influence functions
• cross-fitted one-step estimators
• additive parameterization of the survival function
• effect modifier subsets