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Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure

Publié dansJournal of clinical epidemiology, vol. 64, no. 9, p. 993-1000
Date de publication2011
Résumé

Logistic regression is commonly used in health research, and it is important to be sure that the parameter estimates can be trusted. A common problem occurs when the outcome has few events; in such a case, parameter estimates may be biased or unreliable. This study examined the relation between correctness of estimation and several data characteristics: number of events per variable (EPV), number of predictors, percentage of predictors that are highly correlated, percentage of predictors that were non-null, size of regression coefficients, and size of correlations.

Mots-clés
  • Bias (Epidemiology)
  • Computer Simulation
  • Humans
  • Logistic Models
  • Research Design
  • Statistics as Topic/methods
Citation (format ISO)
COURVOISIER, Delphine et al. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. In: Journal of clinical epidemiology, 2011, vol. 64, n° 9, p. 993–1000. doi: 10.1016/j.jclinepi.2010.11.012
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Article (Published version)
accessLevelRestricted
Identifiants
ISSN du journal0895-4356
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Informations techniques

Création19.11.2012 15:16:00
Première validation19.11.2012 15:16:00
Heure de mise à jour14.03.2023 19:56:20
Changement de statut14.03.2023 19:56:20
Dernière indexation16.01.2024 00:53:50
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