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A comparison of statistical approaches for analysing missing longitudinal patient reported outcome data in randomised controlled trials

Abstract:
Missing data are a potential source of bias in the results of randomised controlled trials (RCTs), but are generally unavoidable in clinical research, particularly in patient reported outcome measures (PROMs). For longitudinally collected outcomes, often only a small subset of participants will have complete data for all relevant time points. Approaches to handling missing longitudinal data include maximum likelihood (ML), multiple imputation (MI) and inverse probability weighting (IPW).
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1186/s13063-017-1902-y

Authors


More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Population Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Population Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Population Health
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Population Health
Publisher:
BioMed Central Publisher's website
Journal:
Trials Journal website
Volume:
18
Issue:
S1
Pages:
200:P325
Publication date:
2017-05-08
Acceptance date:
2017
DOI:
ISSN:
1745-6215
Pubs id:
pubs:734967
URN:
uri:3e843943-baca-4140-95c5-a917fd00f9ba
UUID:
uuid:3e843943-baca-4140-95c5-a917fd00f9ba
Local pid:
pubs:734967

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