Data-Adaptive and Model-Robust Covariate Adjustment for Time-to-Event Outcomes in Stratified Randomized Trials

2026-04-30

AI Summary

This study addresses the challenge of efficiently leveraging high-dimensional baseline covariates to improve estimation efficiency for time-to-event outcomes in stratified randomized clinical trials while avoiding bias due to model misspecification. The authors propose a data-adaptive, model-robust covariate adjustment method built upon the targeted minimum loss-based estimation (TMLE) framework. This approach automatically selects important covariates and incorporates stratification information without requiring prespecification of key variables. It ensures consistent estimation of survival curve functionals while substantially enhancing statistical efficiency. Simulation studies and empirical analyses demonstrate that the method yields more precise estimates in a robust and straightforward manner, even when the relevant covariate set is unknown a priori.

Abstract

Time-to-event outcomes are commonly used as primary endpoints in randomized clinical trials. Despite this, relatively little work incorporates baseline covariate information while also accounting for stratified randomization, a common form of randomization. Moreover, leveraging efficiency gains using these approaches typically requires pre-specifying a subset of covariates that are most predictive of the outcome -- a challenging task in practice, as most trials collect dozens of potentially prognostic baseline variables. In this work, we build on existing literature to propose a data-adaptive and model-robust covariate adjustment method for time-to-event outcomes. Our approach, based on targeted minimum loss-based estimation, allows for data-adaptive covariate selection and model-robust efficient inference on functionals of the survival curve while accounting for stratification. Through extensive simulations and analysis, we showcase the simplicity and improved precision of our method when the covariate set is not known a priori.

Problem

• time-to-event outcomes
• stratified randomized trials
• covariate adjustment
• data-adaptive
• model-robust

Innovation

• data-adaptive
• model-robust
• covariate adjustment
• time-to-event
• targeted minimum loss-based estimation