2026/04/09 by Daniel Backenroth, Sanne Roels, Shiva Dibaj +3 · 1 voice
Mathematics · #Statistical Methods in Clinical Trials #Advanced Causal Inference Techniques #Statistical Methods and Inference
paper · doi:10.1093/biomtc/ujag109
openalex publication_date 2026/04/09 · openalex created_date 2026/06/18 · openalex updated_date 2026/07/22
Adjusting for prognostic baseline covariates in the design and analysis of randomized controlled trials can increase statistical power and improve the precision of treatment effect estimates by reducing outcome variability. In this paper, we describe how the covariate-adjusted log-rank test can be used to enhance the power of late-phase oncology studies with primary time-to-event endpoints. Using a case study of a metastatic castration-resistant prostate cancer trial with overall survival as the primary endpoint, we demonstrate how to estimate variance reductions achievable through different covariate adjustment strategies, including adjustment for a prognostic score, based on historical data. We also compare these strategies with respect to Type I error rate control. We further describe a method that can be used to prevent Type I error rate inflation that can arise when naively embedding covariate adjustment within a group sequential design. A simulation study illustrates the performance of this method across scenarios with different prognostic covariate strength and different numbers of events, randomization strata, and interim analyses. Finally, we present a re-analysis of the overall survival endpoint from a trial in metastatic colorectal cancer using covariate adjustment in a group sequential design.