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

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
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
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Article (Published version)
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Identifiers
ISSN of the journal0895-4356
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Technical informations

Creation11/19/2012 3:16:00 PM
First validation11/19/2012 3:16:00 PM
Update time03/14/2023 7:56:20 PM
Status update03/14/2023 7:56:20 PM
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