Cited by 165Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

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Multiple imputation of missing repeated outcome measurements did not add to linear mixed-effects models

Peters, Sanne A.E., Bots, Michiel L., den Ruijter, Hester M., Palmer, Mike K., Grobbee, Diederick E., Crouse, John R., O’Leary, Daniel H., Evans, Gregory W., Raichlen, Joel S., Moons, Karel G.M., Koffijberg, Hendrik·2012·Journal of Clinical Epidemiology

DOI 10.1016/j.jclinepi.2011.11.012

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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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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: Peters, Sanne A.E.
Bots, Michiel L.
Palmer, Mike K.
Grobbee, Diederick E.
Evans, Gregory W.
Moons, Karel G.M.
Koffijberg, Hendrik
Venue: Journal of Clinical Epidemiology
Publisher: Elsevier BV
Published: 2012-06
Volume: 65
Issue: 6
Pages: 686-695
ISSN: 0895-4356 (ISSN-L)
DOI: 10.1016/j.jclinepi.2011.11.012
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Multiple imputation of missing repeated outcome measurements did not add to linear mixed-effects models

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