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

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 cha...

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Bibliographic Details
Published in:Statistical methods in medical research 2015-12, Vol.24 (6), p.769-787
Main Authors: van den Hout, Ardo, Fox, Jean-Paul, Klein Entink, Rinke H
Format: Article
Language:English
Subjects:
Aged
Aging
Bayes Theorem
Bayesian analysis
Candidates
Cognition
Cognition & reasoning
Cognition Disorders - etiology
Cognition Disorders - mortality
Cognitive ability
Cognitive functioning
Cognitive impairment
Computer simulation
Death
Death & dying
Economic models
Historical perspectives
Humans
Illnesses
Markov analysis
Markov Chains
Medical research
Minimental State Examination
Models, Statistical
Monte Carlo Method
Monte Carlo simulation
Public health
Random effects
Risk analysis
Risk Factors
Statistical inference
Statistical methods
Stroke
Stroke - complications
Stroke - mortality
Time dependence
Time Factors
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Summary: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).
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280211426359