Journal article
Comparison of statistical approaches for analyzing incomplete longitudinal patient-reported outcome data in randomized controlled trials
- Abstract:
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Purpose
Missing data are a potential source of bias in the results of randomized controlled trials (RCTs), but are often unavoidable in clinical research, particularly in patient-reported outcome measures (PROMs). Maximum likelihood (ML), multiple imputation (MI), and inverse probability weighting (IPW) can be used to handle incomplete longitudinal data. This paper compares their performance when analyzing PROMs, using a simulation study based on one RCT dataset.
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.2147/PROM.S147790
Authors
Funding
Bibliographic Details
- Publisher:
- Dove Medical Press
- Journal:
- Patient Related Outcome Measures More from this journal
- Volume:
- 2018
- Issue:
- 9
- Pages:
- 197—209
- Publication date:
- 2018-06-21
- Acceptance date:
- 2018-03-15
- DOI:
- EISSN:
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1179-271X
Item Description
- Keywords:
- Pubs id:
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pubs:858454
- UUID:
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uuid:11c28884-8827-4987-b94b-9f2c93bf5419
- Local pid:
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pubs:858454
- Source identifiers:
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858454
- Deposit date:
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2018-06-20
Terms of use
- Copyright holder:
- Rombach et al
- Copyright date:
- 2018
- Notes:
- © 2018 Rombach et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
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