Proportional Hazards Model with a Change Point for Clustered Event Data

• Activity Number: 697
• Type: Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yu Deng* and Donglin Zeng and Jianwen Cai
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina and The University of North Carolina at Chapel Hill
• Keywords: Clustered event ; Proportional hazards model ; Change point ; m out of n bootstrap

Abstract

The standard Cox model assumes linear effects of the continuous risk factors on the log-hazard rate of disease incidence. This assumption, however, does not hold when some risk factor possesses a threshold effect on disease incidence. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of the continuous variable as a change point in the regression. The marginal pseudo-partial likelihood functions are maximized for estimating both the regression coefficients and the change point. We develop a supremum test based on based on robust score statistics to test the existence of the change point. The inference for the change point estimator is based on the m out of n bootstrap. We establish the consistency and asymptotic distributions of the proposed estimators. The small-sample performance of the proposed method is demonstrated via extensive simulation studies. Finally, the Strong Heart Family Study dataset is analyzed to illustrate the methods.