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Title

Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure
Authors
Courvoisier, Delphine
Combescure, Christophe
Agoritsas, Thomas
Gayet-Ageron, Angèle
Perneger, Thomas
Published in Journal of Clinical Epidemiology. 2011, vol. 64, no. 9, p. 993-1000
Abstract 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.
Keywords Bias (Epidemiology) — Computer Simulation — Humans — Logistic Models — Research Design — Statistics as Topic/methods
Identifiers
PMID: 21411281
Full text
Article (Published version) (1.1 MB) - document accessible for UNIGE members only Limited access to UNIGE
Structures
Faculté de médecine / Section de médecine clinique / Département de médecine interne des spécialités
Research group Epidémiologie clinique (115)
Citation
(ISO format) 		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. https://archive-ouverte.unige.ch/unige:25409

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Deposited on : 2013-01-11

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