Joint modelling of longitudinal binary data and survival data

Yi Ting Hwang, Chia Hui Huang, Chun Chao Wang, Tzu Yin Lin, Yi Kuan Tseng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model.

Original languageEnglish
Pages (from-to)2357-2371
Number of pages15
JournalJournal of Applied Statistics
Volume46
Issue number13
DOIs
StatePublished - 3 Oct 2019

Keywords

  • Cox model
  • generalized linear model
  • metropolis-hastings algorithm
  • Monte Carlo EM algorithm
  • quality of life

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