Professional article

English

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

ContributorsCourvoisier, Delphine

; Combescure, Christophe

; Agoritsas, Thomas

; Gayet-Ageron, Angèle

; Perneger, Thomas

Published inJournal of clinical epidemiology, vol. 64, no. 9, p. 993-1000

Publication date2011

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

Affiliation entities

Faculté de médecine / Section de médecine clinique / Département de médecine

Research groups

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. doi: 10.1016/j.jclinepi.2010.11.012

Article (Published version)

Identifiers

PID : unige:25409
DOI : 10.1016/j.jclinepi.2010.11.012
PMID : 21411281

Journal ISSN0895-4356

726views

1downloads

Creation19/11/2012 15:16:00

First validation19/11/2012 15:16:00

Update14/03/2023 19:56:20

Status update14/03/2023 19:56:20

Last indexation14/03/2025 17:03:47