Competing risk of mortality in association studies of non-fatal events.

In geriatric research of non-fatal events, participants often die during the study follow-up without having the non-fatal event of interest. Cause-specific (CS) hazard regression and Fine-Gray (FG) subdistribution hazard regression are the two most common estimation approaches addressing such compet...

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Autor principal:	Petra Buzkova
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1b152f70769a47f3ba61f09fb077c116
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spelling	oai:doaj.org-article:1b152f70769a47f3ba61f09fb077c1162021-12-02T20:18:10ZCompeting risk of mortality in association studies of non-fatal events.1932-620310.1371/journal.pone.0255313https://doaj.org/article/1b152f70769a47f3ba61f09fb077c1162021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255313https://doaj.org/toc/1932-6203In geriatric research of non-fatal events, participants often die during the study follow-up without having the non-fatal event of interest. Cause-specific (CS) hazard regression and Fine-Gray (FG) subdistribution hazard regression are the two most common estimation approaches addressing such competing risk. We explain how the conventional CS approach and the FG approach differ and why many FG estimates of associations are counter-intuitive. Additionally, we clarify the indirect link between models for hazard and models for cumulative incidence. The methodologies are contrasted on data from the Cardiovascular Health Study, a population-based study in adults aged 65 years and older.Petra BuzkovaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255313 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Petra Buzkova Competing risk of mortality in association studies of non-fatal events.
description	In geriatric research of non-fatal events, participants often die during the study follow-up without having the non-fatal event of interest. Cause-specific (CS) hazard regression and Fine-Gray (FG) subdistribution hazard regression are the two most common estimation approaches addressing such competing risk. We explain how the conventional CS approach and the FG approach differ and why many FG estimates of associations are counter-intuitive. Additionally, we clarify the indirect link between models for hazard and models for cumulative incidence. The methodologies are contrasted on data from the Cardiovascular Health Study, a population-based study in adults aged 65 years and older.
format	article
author	Petra Buzkova
author_facet	Petra Buzkova
author_sort	Petra Buzkova
title	Competing risk of mortality in association studies of non-fatal events.
title_short	Competing risk of mortality in association studies of non-fatal events.
title_full	Competing risk of mortality in association studies of non-fatal events.
title_fullStr	Competing risk of mortality in association studies of non-fatal events.
title_full_unstemmed	Competing risk of mortality in association studies of non-fatal events.
title_sort	competing risk of mortality in association studies of non-fatal events.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1b152f70769a47f3ba61f09fb077c116
work_keys_str_mv	AT petrabuzkova competingriskofmortalityinassociationstudiesofnonfatalevents
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Competing risk of mortality in association studies of non-fatal events.

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