Randomization-Based Covariance Analysis for Confidence Intervals of Treatment Comparisons Based on Restricted Mean Survival Time With Categorized Time-to-Event Data.

This paper introduces a randomization-based method for covariate-adjusted comparisons of restricted mean survival time (RMST) between treatment arms in randomized controlled trials. Existing approaches for covariate-adjusted RMST analysis have model-based assumptions that may not be compatible with the complexity of survival data. We estimate the treatment difference in RMST using randomization-based analysis of covariance (RB-ANCOVA) for categorized time-to-event data by constraining the covariate mean differences between treatment groups to zero. Accordingly, we provide corresponding confidence intervals that offer greater precision than those based on unadjusted RMST differences. The methodology is detailed for comparing two treatment groups for a single or multiple time intervals, as well as for multiple treatment groups over a single interval. We demonstrate the application of these methods using data from a randomized, double-blind, placebo-controlled clinical trial evaluating three doses of a test treatment for ALS.

Duke Scholars

Author Taylor Krajewski Biostatistics & Bioinformatics, Division of Biostatistics