A Unified Approach to Covariate Adjustment for Survival Endpoints in Randomized Clinical Trials

TL;DR

This paper introduces a simple, widely applicable augmentation method for covariate adjustment in survival endpoints of randomized trials, improving efficiency with minimal complexity.

Contribution

It proposes an easy-to-understand augmentation approach for covariate adjustment in survival analysis, compatible with various effect measures and implementable with machine learning.

Findings

Simulation shows substantial efficiency gains.

Method preserves estimator interpretation and asymptotic properties.

Implemented in R package 'sleete' with real data examples.

Abstract

Covariate adjustment aims to improve the statistical efficiency of randomized trials by incorporating information from baseline covariates. Popular methods for covariate adjustment include analysis of covariance for continuous endpoints and standardized logistic regression for binary endpoints. For survival endpoints, while some covariate adjustment methods have been developed for specific effect measures, they are not commonly used in practice for various reasons, including high demands for theoretical and methodological sophistication as well as computational skills. This article describes an augmentation approach to covariate adjustment for survival endpoints that is relatively easy to understand and widely applicable to different effect measures. This approach involves augmenting a given treatment effect estimator in a way that preserves interpretation, consistency, and asymptotic normality. The optimal augmentation term, which minimizes asymptotic variance, can be estimated using various statistical and machine learning methods. Simulation results demonstrate that the augmentation approach can bring substantial gains in statistical efficiency. This approach has been implemented in an R package named \texttt{sleete}, which is described in detail and illustrated with real data.