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Multiple imputation of missing repeated outcome measurements did not add to linear mixed-effects models
Abstract
Objective To assess the added value of multiple imputation (MI) of missing repeated outcomes measures in longitudinal data sets analyzed with linear mixed-effects (LME) models.Study design and setting Data were used from a trial on the effects of Rosuvastatin on rate of change in carotid intima-media thickness (CIMT). The reference treatment effect was derived from a complete data set. Scenarios and proportions of missing values in CIMT measurements were applied and LME analyses were used before and after MI. The added value of MI, in terms of bias and precision, was assessed using the mean-squared error (MSE) of the treatment effects and coverage of the 95% confidence interval.Results The reference treatment effect was -0.0177 mm/y. The MSEs for LME analysis without and with MI were similar in scenarios with up to 40% missing values. Coverage was large in all scenarios and was similar for LME with and without MI.Conclusion Our study empirically shows that MI of missing end point data before LME analyses does not increase precision in the estimated rate of change in the end point. Hence, MI had no added value in this setting and standard LME modeling remains the method of choice.
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“Linear mixed modelling has been demonstrated to control for missing data, and therefore, multiple imputation was not required (Peters et al., 2012;Twisk et al., 2013).”
From Exercise combined with Acceptance and Commitment Therapy for chronic pain: One-year follow-up from a randomized controlled trial (2024) · cites this paper as Peters et al., 2012
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“Therefore, patterns of missingness were not found to be MNAR and the decision to continue the analysis with the available data was made [84,85].”
From Negative and positive affect regulation in a transdiagnostic internet-based protocol for emotional disorders: Randomized controlled trial (2021) · cites this paper as [85]
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Metadata
- Authors
- Peters, S. A. E., Bots, M. L., den Ruijter, H. M., Palmer, M. K., Grobbee, D. E., Crouse, J. R., O’Leary, D. H., Evans, G. W., Raichlen, J. S., Moons, K. G. M., & Koffijberg, H.
- Affiliations
- University Medical Center Utrecht (NL)
- Keele University (GB)
- Wake Forest University Health Sciences (US)
- Carney Hospital (US)
- Wake Forest University (US)
- AstraZeneca (United States) (US)
- Corresponding author
- Peters, Sanne A.E.
- ORCID
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 65
- Issue
- 6
- Pages
- 686-695
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W1981329116
- Cited by
- 165
- References
- 51
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 15 · 2023: 15 · 2022: 20 · 2021: 11 · 2020: 13 · 2019: 7 · 2018: 15 · 2017: 9 · 2016: 7 · 2015: 10 · 2014: 6 · 2013: 7 · 2012: 3
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion)
- University of Twente Research Information (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- multiple imputation, linear mixed models, longitudinal data, carotid intima-media thickness, rosuvastatin, treatment effect estimation, bias and precision, missing data handling, confidence interval coverage
- MeSH terms
- Rosuvastatin Calcium, Algorithms, Biometry, Clinical Trials as Topic, Female, Fluorobenzenes, Humans, Longitudinal Studies, Male, Middle Aged, Pyrimidines, Research Design, Sulfonamides, Models, Statistical, Confidence Intervals, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Atherosclerosis, Carotid Intima-Media Thickness
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