Cited by 568Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsStatistical Methods in Epidemiology

Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis

Twisk, Jos, de Boer, Michiel, de Vente, Wieke, Heymans, Martijn·2013·Journal of Clinical Epidemiology

Abstract

Background and objectives As a result of the development of sophisticated techniques, such as multiple imputation, the interest in handling missing data in longitudinal studies has increased enormously in past years. Within the field of longitudinal data analysis, there is a current debate on whether it is necessary to use multiple imputations before performing a mixed-model analysis to analyze the longitudinal data. In the current study this necessity is evaluated.Study design and setting The results of mixed-model analyses with and without multiple imputation were compared with each other. Four data sets with missing values were created-one data set with missing completely at random, two data sets with missing at random, and one data set with missing not at random). In all data sets, the relationship between a continuous outcome variable and two different covariates were analyzed: a time-independent dichotomous covariate and a time-dependent continuous covariate.Results Although for all types of missing data, the results of the mixed-model analysis with or without multiple imputations were slightly different, they were not in favor of one of the two approaches. In addition, repeating the multiple imputations 100 times showed that the results of the mixed-model analysis with multiple imputation were quite unstable.Conclusion It is not necessary to handle missing data using multiple imputations before performing a mixed-model analysis on longitudinal data.

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Crossref · 10.1016/j.jclinepi.2013.03.017

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Authors
Twisk, J., de Boer, M., de Vente, W., & Heymans, M.
Affiliations
Vrije Universiteit Amsterdam (NL)
Corresponding author
Twisk, Jos
Venue
Journal of Clinical Epidemiology
Publisher
Elsevier BV
Published
2013-09
Volume
66
Issue
9
Pages
1022-1028
ISSN
0895-4356 (ISSN-L)
OpenAlex
W1974992958
Cited by
568
References
43
Citations by year
2026: 58 · 2025: 68 · 2024: 74 · 2023: 57 · 2022: 61 · 2021: 52 · 2020: 46 · 2019: 45 · 2018: 27 · 2017: 26 · 2016: 21 · 2015: 19 · 2014: 10 · 2013: 4
FWCI
15.69
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Clinical Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • UvA-DARE (University of Amsterdam) (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion)
  • Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
  • VU Research Portal (publishedVersion)
  • Data Archiving and Networked Services (DANS) (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 › Statistical Methods in Epidemiology
Keywords
multiple imputation, missing data, longitudinal analysis, mixed models, missing completely at random, missing at random, missing not at random, time-varying covariates
MeSH terms
Data Interpretation, Statistical, Humans, Longitudinal Studies, Patient Dropouts, Research Design, Models, Statistical

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