Missing data in longitudinal studies: cross-sectional multiple imputation provides similar estimates to full-information maximum likelihood

Ann Epidemiol. 2014 Jan;24(1):75-7. doi: 10.1016/j.annepidem.2013.10.007. Epub 2013 Oct 18.

Abstract

Purpose: The aim of this research was to examine, in an exploratory manner, whether cross-sectional multiple imputation generates valid parameter estimates for a latent growth curve model in a longitudinal data set with nonmonotone missingness.

Methods: A simulated longitudinal data set of N = 5000 was generated and consisted of a continuous dependent variable, assessed at three measurement occasions and a categorical time-invariant independent variable. Missing data had a nonmonotone pattern and the proportion of missingness increased from the initial to the final measurement occasion (5%-20%). Three methods were considered to deal with missing data: listwise deletion, full-information maximum likelihood, and multiple imputation. A latent growth curve model was specified and analysis of variance was used to compare parameter estimates between the full data set and missing data approaches.

Results: Multiple imputation resulted in significantly lower slope variance compared with the full data set. There were no differences in any parameter estimates between the multiple imputation and full-information maximum likelihood approaches.

Conclusions: This study suggested that in longitudinal studies with nonmonotone missingness, cross-sectional imputation at each time point may be viable and produces estimates comparable with those obtained with full-information maximum likelihood. Future research pursuing the validity of this method is warranted.

Keywords: Latent growth curve model; Longitudinal studies; Missing data; Multiple imputation; Statistical; Structural equation model; models.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cross-Sectional Studies*
  • Data Interpretation, Statistical*
  • Humans
  • Longitudinal Studies / statistics & numerical data*
  • Models, Statistical
  • Patient Dropouts
  • Probability
  • Research Design / standards*