Covariate-adjusted log-rank test: guaranteed efficiency gain and universal applicability

Ting Ye, Jun Shao and Yanyao Yi

Biometrika, 2024, vol. 111, issue 2, 691-705

Abstract: SummaryNonparametric covariate adjustment is considered for log-rank-type tests of the 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 the Pocock–Simon 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.

Keywords: Covariate calibration; Minimization; Permuted block; Pitman’s relative efficiency; Stratification; Time-to-event data; Validity and power of tests (search for similar items in EconPapers)
Date: 2024
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