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Sample size for binary logistic prediction models: Beyond events per variable criteria

Abstract:

Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and d...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1177/0962280218784726

Authors


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Role:
Author
ORCID:
0000-0002-5529-1541
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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Centre for Statistics in Medicine
Role:
Author
ORCID:
0000-0002-2772-2316
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
ORCID:
0000-0002-7183-4083
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Name:
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Grant:
918.10.615
Publisher:
SAGE Publications
Journal:
Statistical Methods in Medical Research More from this journal
Volume:
28
Issue:
8
Pages:
2455-2474
Publication date:
2018-07-03
Acceptance date:
2018-06-02
DOI:
EISSN:
1477-0334
ISSN:
0962-2802
Pmid:
29966490
Language:
English
Keywords:
Pubs id:
pubs:865148
UUID:
uuid:1ad92117-5292-4454-a664-8c1936951590
Local pid:
pubs:865148
Source identifiers:
865148
Deposit date:
2018-09-11

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