A Model-Informed Rank Test for Right-Censored Survival Data with Intermediate States

• Activity Number: 596
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Ritesh Ramchandani*+ and David A Schoenfeld and Dianne Finkelstein
• Companies: Harvard University and MGH and Harvard University and MGH and Harvard University
• Keywords: survival analysis ; rank tests ; Wilcoxon test ; multi-state models

Abstract

The generalized Wilcoxon and log-rank tests are commonly used when testing for 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 for panel data, as described by Kalbflesich and Lawless (1985), to the full data set. When censoring precludes the ranking 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 in various settings our test can improve power over the log-rank, and the Gehan and Peto-Peto Wilcoxon tests, while maintaining a type 1 error comparable to those tests. The method is illustrated on a neurological data set.