Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture

Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this pap...

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Main Authors:	Sweeting, Michael J, Thompson, Simon G
Format:	Online
Language:	English
Published:	WILEY-VCH Verlag 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443386/

id	pubmed-3443386
recordtype	oai_dc
spelling	pubmed-34433862012-09-17 Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture Sweeting, Michael J Thompson, Simon G Research Articles Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this paper, we first compare the shared random effects model with two approximate approaches: a naïve proportional hazards model with time-dependent covariate and a two-stage joint model, which uses plug-in estimates of the fitted values from a longitudinal analysis as covariates in a survival model. We show that the approximate approaches should be avoided since they can severely underestimate any association between the current underlying longitudinal value and the event hazard. We present classical and Bayesian implementations of the shared random effects model and highlight the advantages of the latter for making predictions. We then apply the models described to a study of abdominal aortic aneurysms (AAA) to investigate the association between AAA diameter and the hazard of AAA rupture. Out-of-sample predictions of future AAA growth and hazard of rupture are derived from Bayesian posterior predictive distributions, which are easily calculated within an MCMC framework. Finally, using a multivariate survival sub-model we show that underlying diameter rather than the rate of growth is the most important predictor of AAA rupture. WILEY-VCH Verlag 2011-09 2011-08-10 /pmc/articles/PMC3443386/ /pubmed/21834127 http://dx.doi.org/10.1002/bimj.201100052 Text en Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Sweeting, Michael J Thompson, Simon G
spellingShingle	Sweeting, Michael J Thompson, Simon G Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
author_facet	Sweeting, Michael J Thompson, Simon G
author_sort	Sweeting, Michael J
title	Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
title_short	Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
title_full	Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
title_fullStr	Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
title_full_unstemmed	Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
title_sort	joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
description	Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this paper, we first compare the shared random effects model with two approximate approaches: a naïve proportional hazards model with time-dependent covariate and a two-stage joint model, which uses plug-in estimates of the fitted values from a longitudinal analysis as covariates in a survival model. We show that the approximate approaches should be avoided since they can severely underestimate any association between the current underlying longitudinal value and the event hazard. We present classical and Bayesian implementations of the shared random effects model and highlight the advantages of the latter for making predictions. We then apply the models described to a study of abdominal aortic aneurysms (AAA) to investigate the association between AAA diameter and the hazard of AAA rupture. Out-of-sample predictions of future AAA growth and hazard of rupture are derived from Bayesian posterior predictive distributions, which are easily calculated within an MCMC framework. Finally, using a multivariate survival sub-model we show that underlying diameter rather than the rate of growth is the most important predictor of AAA rupture.
publisher	WILEY-VCH Verlag
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443386/
_version_	1611909148682747904

Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture

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