Versatile Tests for Comparing Survival Curves Based on Weighted Logrank Statistics

• Activity Number: 702
• Type: Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Theodore Karrison*
• Companies: The University of Chicago
• Keywords: survival curves ; logrank test ; non-proportional hazards ; power

Abstract

The logrank (LR) test is perhaps the most commonly used nonparametric method for comparing two survival curves and yields maximum power under proportional hazards (PH) alternatives. While PH is a reasonable assumption, it need not, of course, hold. Several authors have therefore developed versatile tests using combinations of weighted LR statistics that are more sensitive to non-PH hazards. For example, JW Lee (1996) and S-H Lee (2007) proposed tests based on the G(rho,gamma) family. In this talk we consider Zm=max(|Z1|,|Z2|,|Z3|), where Z1, Z2, and Z3 are z-statistics obtained from G(0,0), G(1,0), and G(0,1) tests, respectively. G(0,0) corresponds to the LR test while G(1,0) and G(0,1) are more sensitive to early and late difference alternatives. Simulation results indicate that the method based on Zm maintains the type I error rate and provides increased power relative to the LR test under early and late difference alternatives, and entails only a small to moderate power loss compared to the more optimally chosen test. The syntax for a Stata command to implement the method is described, and the user can specify other choices for rho and gamma. Design implications are discussed.