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No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

Overview of attention for article published in BMC Medical Research Methodology, November 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#9 of 1,923)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
291 tweeters
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
193 Dimensions

Readers on

mendeley
327 Mendeley
citeulike
2 CiteULike
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Title
No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
Published in
BMC Medical Research Methodology, November 2016
DOI 10.1186/s12874-016-0267-3
Pubmed ID
Authors

Maarten van Smeden, Joris A. H. de Groot, Karel G. M. Moons, Gary S. Collins, Douglas G. Altman, Marinus J. C. Eijkemans, Johannes B. Reitsma

Abstract

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Twitter Demographics

The data shown below were collected from the profiles of 291 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 327 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Unknown 326 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 68 21%
Researcher 52 16%
Student > Master 40 12%
Student > Bachelor 27 8%
Other 20 6%
Other 59 18%
Unknown 61 19%
Readers by discipline Count As %
Medicine and Dentistry 88 27%
Nursing and Health Professions 23 7%
Social Sciences 17 5%
Psychology 17 5%
Mathematics 14 4%
Other 78 24%
Unknown 90 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 192. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 July 2022.
All research outputs
#155,380
of 21,749,791 outputs
Outputs from BMC Medical Research Methodology
#9
of 1,923 outputs
Outputs of similar age
#4,494
of 425,144 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 138 outputs
Altmetric has tracked 21,749,791 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,923 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 425,144 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.