title: Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  It is not necessary to handle missing data using multiple imputations before performing a mixed-model analysis on longitudinal data.
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Centre, de Boelelaan 1118, Amsterdam 1081 HV, The Netherlands. jwr.twisk@vumc.nl
authors.forename: Jos
authors.initials: J
authors.lastname: Twisk
authors.forename: Michiel
authors.initials: M
authors.lastname: de Boer
authors.forename: Wieke
authors.initials: W
authors.lastname: de Vente
authors.forename: Martijn
authors.initials: M
authors.lastname: Heymans
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts; D012107:Research Design / Q000592:standards*
keywords: Longitudinal studies; Missing data mechanisms; Missing data patterns; Mixed models; Multiple imputation; Statistical methods