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

  • Courvoisier D
  • Combescure C
  • Agoritsas T
 et al. 
  • 104


    Mendeley users who have this article in their library.
  • 26


    Citations of this article.


Objective: 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. Study Design: Simulation studies. Results: In many situations, logistic regression modeling may pose substantial problems even if the number of EPV exceeds 10. Moreover, the number of EPV is not the only element that impacts on the correctness of parameter estimation. High regression coefficients and high correlations between the predictors may cause large problems in the estimation process. Finally, power is generally very low, even at 20 EPV. Conclusion: There is no single rule based on EPV that would guarantee an accurate estimation of logistic regression parameters. Instead, the number of predictors, probable size of the regression coefficients based on previous literature, and correlations among the predictors must be taken into account as guidelines to determine the necessary sample size. © 2011 Elsevier Inc. All rights reserved.

Author-supplied keywords

  • Event per variable
  • Logistic regression
  • Model adequacy
  • Model building
  • Power
  • Type I error

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text


  • Delphine S. Courvoisier

  • Christophe Combescure

  • Thomas Agoritsas

  • Angle Gayet-Ageron

  • Thomas V. Perneger

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free