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A comparison of model selection methods for prediction in the presence of multiply imputed data

Abstract:

Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e.g. urn:x-wi...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1002/bimj.201700232

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ORCID:
0000-0001-9661-6835
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDM
Subgroup:
Tropical Medicine
ORCID:
0000-0002-2740-3155
Publisher:
Wiley‐VCH Verlag Publisher's website
Journal:
Biometrical Journal Journal website
Volume:
61
Issue:
2
Pages:
343-356
Publication date:
2018-10-23
Acceptance date:
2018-09-24
DOI:
EISSN:
1521-4036
ISSN:
0323-3847
Pubs id:
pubs:939542
URN:
uri:2535c1f7-6f06-4ad9-a8e9-5445aa593266
UUID:
uuid:2535c1f7-6f06-4ad9-a8e9-5445aa593266
Local pid:
pubs:939542

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