Title: The joint model for the survival data and binary repeated measures
Authors: Yi-Ting Hwang - National Taipei University (Taiwan) [presenting]
Chia-Hui Huang - National Taipei University (Taiwan)
Chun-Chao Wang - National Taipei University (Taiwan)
Yi-Kang Tseng - National Central University (Taiwan)
Abstract: The medical cost in an aging society will increase substantially if the elderly have higher incidence of chronic diseases, disability and unable to live independently. Healthy lifestyle not only affects elderly individuals but also influence the entire community. When assessing the healthy lifestyle, survival and quality of life should be considered concurrently. Thus, simultaneously identifying the association between the survival and long-term quality of life becomes an important issue. Jointly modeling two outcomes have been studied previously. Most of the existing models have a sequence of continuous repeated measurements, which are modeled by the general linear model for longitudinal data. A modified joint model for modeling survival and the longitudinal binary repeated measures simultaneously is proposed. The joint likelihood estimation is used. 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. Monte Carlo simulations are used to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. A real data is used to illustrate the feasibility of the proposed model.