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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...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2008
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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. |
format | Text |
id | pubmed-2447577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT barnettadrian competingrisksmodelsandtimedependentcovariates AT gravesnick competingrisksmodelsandtimedependentcovariates |