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The comparative performance of logistic regression and random forest in propensity score methods: A simulation study

Abstract:
Propensity scores (PS) are typically estimated using logistic regression (LR). Machine learning techniques such as random forests (RF) have been suggested as promising alternatives for variable selection and PS estimation.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/pds.4275

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
NDORMS
Role:
Author
Publisher:
Wiley Publisher's website
Journal:
33rd International Conference on Pharmacoepidemiology and Therapeutic Risk Management(ICPE 2017) Journal website
Volume:
26
Issue:
S2
Pages:
489
Series:
33rd International Conference on Pharmacoepidemiology and Therapeutic Risk Management, Palais des congrès de Montréal, Montréal, Canada, August 26‐30, 2017
Host title:
Pharmacoepidemiology and Drug Safety
Publication date:
2017-08-22
Acceptance date:
2017-04-01
DOI:
Source identifiers:
709988
Pubs id:
pubs:709988
UUID:
uuid:3636bc63-91a7-4625-85e6-15f2375745c5
Local pid:
pubs:709988
Deposit date:
2018-02-01

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