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

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

van den Hout, A; Fox, JP; Klein Entink, RH; (2015) Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor. Statistical Methods in Medical Research , 24 (6) pp. 769-787. 10.1177/0962280211426359. Green open access

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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).

Type:	Article
Title:	Bayesian inference for an illness-death model for stroke with cognition as a latent time-dependent risk factor.
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1177/0962280211426359
Publisher version:	http://dx.doi.org/10.1177/0962280211426359
Language:	English
Additional information:	This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).
Keywords:	item-response theory, Markov chain Monte Carlo, mini-mental state examination, multi-state model, random effects
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI:	https://discovery.ucl.ac.uk/id/eprint/1339896