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

J Clin Epidemiol. 2013 Sep;66(9):1022-8. doi: 10.1016/j.jclinepi.2013.03.017. Epub 2013 Jun 21.


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.

Keywords: Longitudinal studies; Missing data mechanisms; Missing data patterns; Mixed models; Multiple imputation; Statistical methods.

MeSH terms

  • Data Interpretation, Statistical*
  • Humans
  • Longitudinal Studies*
  • Models, Statistical
  • Patient Dropouts
  • Research Design / standards*