Rombach, I. orcid.org/0000-0003-3464-3867, Jenkinson, C., Gray, A. et al. (2 more authors) (2018) Comparison of statistical approaches for analyzing incomplete longitudinal patient-reported outcome data in randomized controlled trials. Patient Related Outcome Measures, 9. pp. 197-209. ISSN 1179-271X
Abstract
Purpose: Missing data are a potential source of bias in the results of 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 an RCT data set.
Methods: Realistic missing-at-random data were simulated based on patterns observed during the follow-up of the knee arthroscopy trial (ISRCTN45837371). Simulation scenarios covered different sample sizes, with missing PROM data in 10%–60% of participants. Monotone and nonmonotone missing data patterns were considered. Missing data were addressed by using ML, MI, and IPW and analyzed via multilevel mixed-effects linear regression models. Root mean square errors in the treatment effects were used as performance parameters across 1,000 simulations.
Results: Nonconvergence issues were observed for IPW at small sample sizes. The performance of all three approaches worsened with decreasing sample size and increasing proportions of missing data. MI and ML performed similarly when the MI model was restricted to baseline variables, but MI performed better when using postrandomization data in the imputation model and also in nonmonotone versus monotone missing data scenarios. IPW performed worse than ML and MI in all simulation scenarios.
Conclusion: When additional postrandomization information is available, MI can be beneficial over ML for handling incomplete longitudinal PROM data. IPW is not recommended for handling missing PROM data in the simulated scenarios.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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. (https://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. |
Keywords: | missing data; repeated measures; patient-reported outcome measures; PROMS; multilevel mixed-effects models; multiple imputation; inverse probability weighting |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Nov 2021 16:15 |
Last Modified: | 08 Nov 2021 16:15 |
Status: | Published |
Publisher: | Dove Medical Press |
Refereed: | Yes |
Identification Number: | 10.2147/prom.s147790 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180197 |
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