title: A model-informed rank test for right-censored data with intermediate states.
abstract: The generalized Wilcoxon and log-rank tests are commonly used for testing differences between two survival distributions. We modify the Wilcoxon test to account for auxiliary information on intermediate disease states that subjects may pass through before failure. For a disease with multiple states where patients are monitored periodically but exact transition times are unknown (e.g. staging in cancer), we first fit a multi-state Markov model to the full data set; when censoring precludes the comparison of survival times between two subjects, we use the model to estimate the probability that one subject will have survived longer than the other given their censoring times and last observed status, and use these probabilities to compute an expected rank for each subject. These expected ranks form the basis of our test statistic. Simulations demonstrate that the proposed test can improve power over the log-rank and generalized Wilcoxon tests in some settings while maintaining the nominal type 1 error rate. The method is illustrated on an amyotrophic lateral sclerosis data set.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, U.S.A.
authors.forename: Ritesh
authors.initials: R
authors.lastname: Ramchandani
authors.forename: Dianne M
authors.initials: DM
authors.lastname: Finkelstein
authors.forename: David A
authors.initials: DA
authors.lastname: Schoenfeld
mesh_terms: D000690:Amyotrophic Lateral Sclerosis; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D018450:Disease Progression; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D016019:Survival Analysis
keywords: Gehan-Wilcoxon; auxiliary information; multi-state models; rank test; survival