Semiparametric Hazards Rate Model for Modeling Short-Term and Long-Term Effects

• Activity Number: 619
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Guoqing Diao*+ and Donglin Zeng
• Companies: George Mason University and The University of North Carolina at Chapel Hill
• Keywords: Non-parametric likelihood ; Proportional hazards model ; Proportional odds model ; Semiparametric efficiency

Abstract

We propose a novel semiparametric hazards rate model to incorporate the short-term and long-term effects of potentially time-dependent covariates on failure time data. Under the proposed model, the hazard ratios between two sets of covariate values are allowed to be non-constant over time. The proposed model also provides an appealing diagnostic tool for testing the proportional hazards model and the proportional odds model. We develop efficient likelihood-based estimation and inference procedures. The nonparametric maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Extensive simulation studies demonstrate that the proposed methods perform well in practical settings. The proposed method successfully captured the phenomenon of crossing hazards in a cancer clinical trial and identified a genetic marker with significant long-term effect missed by using the proportional hazards model on age-at-onset of alcoholism in a genetic study.