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

TL;DR

This paper introduces a data-adaptive, model-robust covariate adjustment method for time-to-event outcomes in stratified randomized trials, improving efficiency without prior covariate selection.

Contribution

It proposes a novel targeted minimum loss-based estimation approach that adapts covariate selection and accounts for stratification, enhancing inference accuracy.

Findings

Method improves precision in estimating survival curves.

Simulation studies demonstrate robustness and simplicity.

Approach effectively handles many prognostic variables without pre-selection.

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.