A Bayesian multivariate spatial approach for illness-death survival models

Illness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider th...

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Detalles Bibliográficos
Autores: Llopis-Cardona F, Armero C, Sanfélix-Gimeno G
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
Repositorio:r-FISABIO. Repositorio Institucional de Producción Científica
OAI Identifier:oai:fisabio.fundanetsuite.com:p15423
Acceso en línea:https://fisabio.portalinvestigacion.com/publicaciones/15423
Access Level:acceso abierto
Palabra clave:Bayesian inference
integrated nested Laplace approximation
multi-state models
spatial correlation
transition probabilities
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spelling A Bayesian multivariate spatial approach for illness-death survival modelsLlopis-Cardona FArmero CSanfélix-Gimeno GBayesian inferenceintegrated nested Laplace approximationmulti-state modelsspatial correlationtransition probabilitiesIllness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider the competing risk of death but also allow us to study the progression from illness to death. The intensity of each transition can be modelled including both fixed and random effects of covariates. In particular, spatially structured random effects or their multivariate versions can be used to assess spatial differences between regions and among transitions. We propose a Bayesian methodological framework based on an illness-death model with a multivariate Leroux prior for the random effects. We apply this model to a cohort study regarding progression after an osteoporotic hip fracture in elderly patients. From this spatial illness-death model, we assess the geographical variation in risks, cumulative incidences and transition probabilities related to recurrent hip fracture and death. Bayesian inference is done via the integrated nested Laplace approximation.SAGE PUBLICATIONS LTD2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fisabio.portalinvestigacion.com/publicaciones/15423STATISTICAL METHODS IN MEDICAL RESEARCHISSN: 09622802ISSNe: 14770334reponame:r-FISABIO. Repositorio Institucional de Producción Científicainstname:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)Inglésinfo:eu-repo/semantics/openAccessoai:fisabio.fundanetsuite.com:p154232026-06-11T12:45:17Z
dc.title.none.fl_str_mv A Bayesian multivariate spatial approach for illness-death survival models
title A Bayesian multivariate spatial approach for illness-death survival models
spellingShingle A Bayesian multivariate spatial approach for illness-death survival models
Llopis-Cardona F
Bayesian inference
integrated nested Laplace approximation
multi-state models
spatial correlation
transition probabilities
title_short A Bayesian multivariate spatial approach for illness-death survival models
title_full A Bayesian multivariate spatial approach for illness-death survival models
title_fullStr A Bayesian multivariate spatial approach for illness-death survival models
title_full_unstemmed A Bayesian multivariate spatial approach for illness-death survival models
title_sort A Bayesian multivariate spatial approach for illness-death survival models
dc.creator.none.fl_str_mv Llopis-Cardona F
Armero C
Sanfélix-Gimeno G
author Llopis-Cardona F
author_facet Llopis-Cardona F
Armero C
Sanfélix-Gimeno G
author_role author
author2 Armero C
Sanfélix-Gimeno G
author2_role author
author
dc.subject.none.fl_str_mv Bayesian inference
integrated nested Laplace approximation
multi-state models
spatial correlation
transition probabilities
topic Bayesian inference
integrated nested Laplace approximation
multi-state models
spatial correlation
transition probabilities
description Illness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider the competing risk of death but also allow us to study the progression from illness to death. The intensity of each transition can be modelled including both fixed and random effects of covariates. In particular, spatially structured random effects or their multivariate versions can be used to assess spatial differences between regions and among transitions. We propose a Bayesian methodological framework based on an illness-death model with a multivariate Leroux prior for the random effects. We apply this model to a cohort study regarding progression after an osteoporotic hip fracture in elderly patients. From this spatial illness-death model, we assess the geographical variation in risks, cumulative incidences and transition probabilities related to recurrent hip fracture and death. Bayesian inference is done via the integrated nested Laplace approximation.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fisabio.portalinvestigacion.com/publicaciones/15423
url https://fisabio.portalinvestigacion.com/publicaciones/15423
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv SAGE PUBLICATIONS LTD
publisher.none.fl_str_mv SAGE PUBLICATIONS LTD
dc.source.none.fl_str_mv STATISTICAL METHODS IN MEDICAL RESEARCH
ISSN: 09622802
ISSNe: 14770334
reponame:r-FISABIO. Repositorio Institucional de Producción Científica
instname:Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
instname_str Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
reponame_str r-FISABIO. Repositorio Institucional de Producción Científica
collection r-FISABIO. Repositorio Institucional de Producción Científica
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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