Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor

Ardo van den Hout, Jean-Paul Fox, Rinke H. Klein Entink

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Longitudinal data can be used to estimate the transition intensities between healthy and unhealthy states prior to death. An illness-death model for history of stroke is presented, where time-dependent transition intensities are regressed on a latent variable representing cognitive function. The change of this function over time is described by a linear growth model with random effects. Occasion-specific cognitive function is measured by an item response model for longitudinal scores on the Mini-Mental State Examination, a questionnaire used to screen for cognitive impairment. The illness-death model will be used to identify and to explore the relationship between occasion-specific cognitive function and stroke. Combining a multi-state model with the latent growth model defines a joint model which extends current statistical inference regarding disease progression and cognitive function. Markov chain Monte Carlo methods are used for Bayesian inference. Data stem from the Medical Research Council Cognitive Function and Ageing Study in the UK (1991–2005).
Original languageEnglish
Pages (from-to)769-787
JournalStatistical methods in medical research
Issue number6
Publication statusPublished - 2015


  • Item-response theory
  • Markov chain Monte Carlo
  • mini-mental state examination
  • Multi-state model
  • Random effects

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