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Stat Methods Med Res. 2015 Dec;24(6):769-87. doi: 10.1177/0962280211426359. Epub 2011 Nov 11.

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

Author information

1
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK. ardo.vandenhout@mrc-bsu.cam.ac.uk.
2
Department of Research Methodology, Measurement, and Data Analysis Faculty of Behavioral Sciences, Twente University, The Netherlands.
3
TNO Quality and Safety, Zeist, The Netherlands.

Abstract

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).

KEYWORDS:

Markov chain Monte Carlo; item-response theory; mini-mental state examination; multi-state model; random effects

PMID:
22080595
PMCID:
PMC4668781
DOI:
10.1177/0962280211426359
[Indexed for MEDLINE]
Free PMC Article
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