A Semiparametric Threshold Regression Analysis with Flexible Covariate Effects

• Activity Number: 326
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Xin He and Ran Ji*+ and Mei-Ling Ting Lee
• Companies: University of Maryland and University of Maryland and University of Maryland
• Keywords: B-spline ; Spline approximation ; Survival analysis ; Threshold regression ; Wiener process

Abstract

Threshold regression is a relatively new alternative approach to the Cox proportional hazards model when the proportional hazards assumption is violated. It is based on first-hitting-time models, where the time-to-event data can be modeled as the time at which the stochastic process of interest first hits a boundary or threshold state. In this paper, we consider a semiparametric threshold regression model with flexible covariate effects. Specifically, we propose a B-spline approximation method to estimate nonlinear covariate effects on both the initial state and the rate of the process. Some graphical and numerical procedures are presented for model checking. Simulation studies are conducted for practical situations, and the methodology is applied to a study of osteoporotic fractures that motivated this investigation.