The versatility of multi-state models for the analysis of longitudinal data with unobservable features

Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighte...

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Detalles Bibliográficos
Autores principales: Farewell, Vernon T., Tom, Brian D. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2012
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3884139/
https://www.ncbi.nlm.nih.gov/pubmed/23225140
http://dx.doi.org/10.1007/s10985-012-9236-2
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author Farewell, Vernon T.
Tom, Brian D. M.
author_facet Farewell, Vernon T.
Tom, Brian D. M.
author_sort Farewell, Vernon T.
collection PubMed
description Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted. In addition, joint modelling of multiple processes is illustrated with application to potentially informative loss to follow-up, mis-measured or missclassified data and causal inference.
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spelling pubmed-38841392014-01-13 The versatility of multi-state models for the analysis of longitudinal data with unobservable features Farewell, Vernon T. Tom, Brian D. M. Lifetime Data Anal Article Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted. In addition, joint modelling of multiple processes is illustrated with application to potentially informative loss to follow-up, mis-measured or missclassified data and causal inference. Springer US 2012-12-06 2014 /pmc/articles/PMC3884139/ /pubmed/23225140 http://dx.doi.org/10.1007/s10985-012-9236-2 Text en © The Author(s) 2012 https://creativecommons.org/licenses/by/2.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Article
Farewell, Vernon T.
Tom, Brian D. M.
The versatility of multi-state models for the analysis of longitudinal data with unobservable features
title The versatility of multi-state models for the analysis of longitudinal data with unobservable features
title_full The versatility of multi-state models for the analysis of longitudinal data with unobservable features
title_fullStr The versatility of multi-state models for the analysis of longitudinal data with unobservable features
title_full_unstemmed The versatility of multi-state models for the analysis of longitudinal data with unobservable features
title_short The versatility of multi-state models for the analysis of longitudinal data with unobservable features
title_sort versatility of multi-state models for the analysis of longitudinal data with unobservable features
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3884139/
https://www.ncbi.nlm.nih.gov/pubmed/23225140
http://dx.doi.org/10.1007/s10985-012-9236-2
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