Joint Models for Longitudinal Binary and Survival Data

• Activity Number: 655
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Yi-Ting Hwang*+ and Tzu-Yin Lin
• Companies: National Taipei University and National Taipei University
• Keywords: quality of life ; two-stage model ; joint model ; EM algorithm ; Metropolis-Hastings algorithm

Abstract

The medical cost will increase substantially if the elderly have higher incidence of chronic diseases, disability and unable to live independently, especially in an aging society. Healthy lifestyle not only affects elderly individuals but also affects the whole community. When assessing the healthy lifestyle, survival and quality of life should be considered simultaneously. Thus, how to simultaneously identify the association between the survival and long-term quality of life becomes an important issue.

The two-stage model proposed by Tsiatis (1995) and the joint model proposed by Tsiatis and Wulfsohn (1997) can be used to model a sequence of repeated continuous measures and survival jointly. The paper proposes a modified two-stage model and a modified joint model for modeling survival and the longitudinal binary covariates simultaneously. Owing to some unobservable information in the model, some parameters in joint model have to be estimated by Monte Carlo EM algorithm and Metropolis-Hastings algorithm. The performance of the proposed model based on the accuracy and precision of the estimates is evaluated by Monte Carlo simulation.