[Poisson regression use in nephrology].

Utilisation de la régression de Poisson en néphrologie.
Competing risk Effet dépendant du temps Epidemiology Poisson regression Registre Registry Risques concurrents Régression de Poisson Time-dependant effect Time-dependent variable Variable dépendante du temps Épidémiologie

Journal

Nephrologie & therapeutique
ISSN: 1872-9177
Titre abrégé: Nephrol Ther
Pays: France
ID NLM: 101248950

Informations de publication

Date de publication:
May 2020
Historique:
received: 27 09 2019
accepted: 30 09 2019
pubmed: 12 4 2020
medline: 29 10 2021
entrez: 12 4 2020
Statut: ppublish

Résumé

Poisson regression is a powerful tool for the analysis of incidence rates from cohort survival studies and facilitates simple, straightforward analyses of temporal patterns that may be difficult to assess with other methods. The Kaplan-Meier method, the logrank test, and the Cox model each have their respective parallels in grouped data analysis: instantaneous hazards, the hazard ratio for grouped data, and Poisson regression, grouped by intervals. This approach makes it possible to present the instantaneous speed of occurrence of events that may be more significant for clinicians and to consider more easily some constraints like parameters according to time (like time dependent variables or a time-dependent effect, neither of which are included in the conventional Cox model). However the application of Poisson regression requires that data on individual subjects be organized into event-time tables stratified by time and other factors of interest. This approach therefore requires the use of a large-scale database when small time intervals or many adjustment variables are necessary.

Identifiants

pubmed: 32276765
pii: S1769-7255(20)30002-X
doi: 10.1016/j.nephro.2019.09.006
pii:
doi:

Types de publication

Journal Article

Langues

fre

Sous-ensembles de citation

IM

Pagination

184-190

Informations de copyright

Copyright © 2020 Société francophone de néphrologie, dialyse et transplantation. Published by Elsevier Masson SAS. All rights reserved.

Auteurs

Cécile Couchoud (C)

Registre REIN, agence de la biomédecine, 1, avenue du Stade-de-France, 93212 Saint-Denis-La Plaine, France; UMR CNRS 5558, laboratoire biostatistique santé, université Claude-Bernard-Lyon I, 43, boulevard du 11 novembre 1918, 69622 Villeurbanne, France. Electronic address: cecile.couchoud@biomedecine.fr.

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Classifications MeSH