- 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
- Files:
-
-
(pdf, 70.1kb)
-
- Publisher copy:
- 10.1186/s13063-017-1902-y
-
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- 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
- Copyright holder:
- Rombach et al.
- Copyright date:
- 2017
- Notes:
- © The Author(s) 2017. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.
Journal article
A comparison of statistical approaches for analysing missing longitudinal patient reported outcome data in randomised controlled trials
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