Covariate-Adjusted Group Sequential Comparisons of Survival Probabilities. Stat Med 2025 Dec;44(28-30):e70339
Date
12/06/2025Pubmed ID
41350757Pubmed Central ID
PMC12822512DOI
10.1002/sim.70339Scopus ID
2-s2.0-105023969704 (requires institutional sign-in at Scopus site)Abstract
In confirmatory clinical trials, survival outcomes are frequently studied and interim analyses for efficacy and/or futility are often desirable. Methods including the log rank test and Cox regression model are commonly used for treatment comparisons, but are most efficient in a proportional hazards (PH) setting and are subject to a loss of power when PH are violated. Such violations may be expected a priori, particularly when the mechanisms of treatments differ such as immunotherapy versus chemotherapy for treating cancer. We propose group sequential tests for comparing survival probabilities with covariate adjustment that allow for interim analyses in the presence of non-PH and offer easily interpreted, clinically meaningful summary measures of the treatment effect. The joint distribution of repeatedly computed test statistics possesses an independent increments structure asymptotically, facilitating marginal comparisons of survival probabilities in sequential studies by simplifying critical value specification to maintain Type I error control and sample size/power determination. Simulations demonstrate that the Type I error rate and power of the proposed tests meet targeted levels and are robust to the PH assumption and covariate influence. The proposed tests are illustrated using the Blood and Marrow Transplant Clinical Trials Network 1101 trial data ( https://www.clinicaltrials.gov #NCT01597778).
Author List
Zhang P, Logan B, Martens MJAuthor
Michael Martens PhD Assistant Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Bone Marrow TransplantationClinical Trials, Phase III as Topic
Computer Simulation
Data Interpretation, Statistical
Humans
Models, Statistical
Multicenter Studies as Topic
Probability
Proportional Hazards Models
Randomized Controlled Trials as Topic
Survival Analysis
