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Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?

Abstract:

Background

Missing outcomes can seriously impair the ability to make correct inferences from randomized controlled trials (RCTs). Complete case (CC) analysis is commonly used, but it reduces sample size and is perceived to lead to reduced statistical efficiency of estimates while increasing the potential for bias. As multiple imputation (MI) methods preserve sample size, they are generally viewed as the preferred analytical approach. We examined this assumption, comparing the per...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1186/s13063-016-1473-3

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Tropical Medicine
Role:
Author
European and Developing Countries Clinical Trials Partnership More from this funder
Publisher:
BioMed Central Publisher's website
Journal:
Trials Journal website
Volume:
17
Issue:
1
Pages:
341
Publication date:
2016-07-01
Acceptance date:
2016-06-29
DOI:
EISSN:
1745-6215
Pmid:
27450066
Language:
English
Keywords:
Pubs id:
pubs:638188
UUID:
uuid:1471adbe-635d-4a29-93e8-bd8cc38e9377
Local pid:
pubs:638188
Source identifiers:
638188
Deposit date:
2017-08-25

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