An Extended Hazard Model with Longitudinal Covariates

• Activity Number: 156
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Korean International Statistical Society
• Author(s): Yu-Ru Su* and Yi-Kuan Tseng and Jane-Ling Wang
• Companies: Fred Hutchinson Cancer Research Center and National Central University and UC Davis
• Keywords: Joint modeling ; Hazard smoothing ; Maximum likelihood estimate ; Semiparametirc likelihood ratio test

Abstract

In clinical trials and other medical studies, it has become increasingly common to observe an event time of interest and longitudinal covariates simultaneously. Joint modeling approaches have been employed to analyze both survival and longitudinal processes and their association. Early attention has mostly been placed on developing adaptive and flexible longitudinal processes based on a pre-specified survival model, most commonly the Cox proportional model. In this paper, we propose a general class of semi-parametric hazard regression models, termed extended hazard model, for the survival component. This class includes the Cox proportional hazards model and the accelerated failure time model as special cases. A pseudo joint likelihood approach is proposed to estimate the unknown parameters and components through a Monte Carlo EM algorithm. Asymptotic theory for the estimators is developed with theory for the semi-parametric likelihood ratio tests. A case study featuring data from a Taiwan HIV/AIDS cohort study further illustrates the usefulness of the extended hazard model.