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Competing risks models and time-dependent covariates

New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to e...

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Detalles Bibliográficos
Autores principales: Barnett, Adrian, Graves, Nick
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447577/
https://www.ncbi.nlm.nih.gov/pubmed/18423067
http://dx.doi.org/10.1186/cc6840
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author Barnett, Adrian
Graves, Nick
author_facet Barnett, Adrian
Graves, Nick
author_sort Barnett, Adrian
collection PubMed
description New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data.
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spelling pubmed-24475772008-07-10 Competing risks models and time-dependent covariates Barnett, Adrian Graves, Nick Crit Care Commentary New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data. BioMed Central 2008 2008-04-11 /pmc/articles/PMC2447577/ /pubmed/18423067 http://dx.doi.org/10.1186/cc6840 Text en Copyright © 2008 BioMed Central Ltd
spellingShingle Commentary
Barnett, Adrian
Graves, Nick
Competing risks models and time-dependent covariates
title Competing risks models and time-dependent covariates
title_full Competing risks models and time-dependent covariates
title_fullStr Competing risks models and time-dependent covariates
title_full_unstemmed Competing risks models and time-dependent covariates
title_short Competing risks models and time-dependent covariates
title_sort competing risks models and time-dependent covariates
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447577/
https://www.ncbi.nlm.nih.gov/pubmed/18423067
http://dx.doi.org/10.1186/cc6840
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