A Markov regression random-effects model for remission of functional disability in patients following a first stroke: A Bayesian approach

Shin Liang Pan, Hui Min Wu, Amy Ming Fang Yen, Tony Hsiu Hsi Chen

Research output: Contribution to journalArticle

19 Citations (Scopus)


Few attempts have been made to model the dynamics of stroke-related disability. It is possible though, using panel data and multi-state Markov regression models that incorporate measured covariates and latent variables (random effects). This study aimed to model a series of functional transitions (following a first stroke) using a three-state Markov model with or without considering random effects. Several proportional hazards parameterizations were considered. A Bayesian approach that utilizes the Markov Chain Monte Carlo (MCMC) and Gibbs sampling functionality of WinBUGS (a Windows-based Bayesian software package) was developed to generate the marginal posterior distributions of the various transition parameters (e.g. the transition rates and transition probabilities). Model building and comparisons was guided by reference to the deviance information criteria (DIC). Of the four proportional hazards models considered, exponential regression was preferred because it led to the smallest deviances. Adding random effects further improved the model fit. Of the covariates considered, only age, infarct size, and baseline functional status were significant. By using our final model we were able to make individual predictions about functional recovery in stroke patients.

Original languageEnglish
Pages (from-to)5335-5353
Number of pages19
JournalStatistics in Medicine
Issue number29
Publication statusPublished - Dec 20 2007
Externally publishedYes



  • Activities of daily living
  • Bayes theorem
  • Cerebrovascular accident
  • Markov chains
  • Monte Carlo method
  • Stochastic processes

ASJC Scopus subject areas

  • Epidemiology

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