Inverse Probability of Treatment Weighting: A Simple and Effective Approach to Covariate Adjustment for Survival Endpoints in Randomized Clinical Trials

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, they are not commonly used in practice for various reasons, including high demands for theoretical and methodological sophistication as well as computational skills. In this article, we point out that inverse probability of treatment weighting (IPTW) is a simple and effective approach to covariate adjustment for survival endpoints. We provide theoretical results that justify its use in conjunction with the Cox model and the Kaplan-Meier analysis, as well as numerical results that demonstrate its effectiveness in finite samples of small to moderate sizes. Compared to other covariate adjustment methods for survival endpoints, IPTW has several major advantages including simplicity, generality, and ease of implementation, and thus holds great promise as a practical approach to covariate adjustment for survival endpoints.