V. Stel, F. Dekker, G. Tripepi, C. Zoccali, and K. Jager. Nephron. Clinical practice, 119 (3):
c255-60(January 2011)6596<m:linebreak></m:linebreak>CI: Copyright (c) 2011; JID: 101159763; 2011/09/14 aheadofprint; ppublish;<m:linebreak></m:linebreak>Anàlisi de supervivència; Introductori; Nefrologia.
DOI: 10.1159/000328916
Abstract
In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the Cox regression method, and to provide some guidance regarding the presentation of the results.
ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. v.s.stel@amc.uva.nl
%0 Journal Article
%1 Stel2011
%A Stel, Vianda S
%A Dekker, Friedo W
%A Tripepi, Giovanni
%A Zoccali, Carmine
%A Jager, Kitty J
%D 2011
%J Nephron. Clinical practice
%K ConfidenceIntervals DataInterpretation GlomerularFiltrationRate Humans KidneyDiseases KidneyDiseases:mortality ProportionalHazardsModels Statistical
%N 3
%P c255-60
%R 10.1159/000328916
%T Survival analysis II: Cox regression.
%U http://www.ncbi.nlm.nih.gov/pubmed/21921637
%V 119
%X In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the Cox regression method, and to provide some guidance regarding the presentation of the results.
%@ 1660-2110; 1660-2110
@article{Stel2011,
abstract = {In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the Cox regression method, and to provide some guidance regarding the presentation of the results.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Stel, Vianda S and Dekker, Friedo W and Tripepi, Giovanni and Zoccali, Carmine and Jager, Kitty J},
biburl = {https://www.bibsonomy.org/bibtex/2595273de3ecccab03293cb8187032e9f/jepcastel},
city = {ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. v.s.stel@amc.uva.nl},
doi = {10.1159/000328916},
interhash = {753d0c2d36434ae80af31e9e8f7696ab},
intrahash = {595273de3ecccab03293cb8187032e9f},
isbn = {1660-2110; 1660-2110},
issn = {1660-2110},
journal = {Nephron. Clinical practice},
keywords = {ConfidenceIntervals DataInterpretation GlomerularFiltrationRate Humans KidneyDiseases KidneyDiseases:mortality ProportionalHazardsModels Statistical},
month = {1},
note = {6596<m:linebreak></m:linebreak>CI: Copyright (c) 2011; JID: 101159763; 2011/09/14 [aheadofprint]; ppublish;<m:linebreak></m:linebreak>Anàlisi de supervivència; Introductori; Nefrologia},
number = 3,
pages = {c255-60},
pmid = {21921637},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Survival analysis II: Cox regression.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21921637},
volume = 119,
year = 2011
}