Covariate-Adjusted Log-Rank Test: Guaranteed Efficiency Gain and Universal Applicability

TL;DR

This paper introduces a covariate-adjusted log-rank test for clinical trial data that guarantees efficiency gains, is universally applicable across various randomization schemes, and is supported by new theoretical and empirical evidence.

Contribution

The paper proposes a simple, explicit covariate-adjusted log-rank test with guaranteed efficiency gains and universal applicability across multiple randomization methods.

Findings

The proposed test has a guaranteed efficiency gain over the unadjusted test.

It maintains validity across different covariate-adaptive randomization schemes.

Empirical results confirm improved power and controlled type I error.

Abstract

Nonparametric covariate adjustment is considered for log-rank type tests of treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our proposed test achieves universal applicability in the sense that the same formula of test can be universally applied to simple randomization and all commonly used covariate-adaptive randomization schemes such as the stratified permuted block and Pocock and Simon's minimization, which is not a property enjoyed by the unadjusted log-rank test. Our method is supported by novel asymptotic theory and empirical results for type I error and power of tests.