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

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

Author information

1
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; Offord Centre for Child Studies, McMaster University, Hamilton, Ontario, Canada. Electronic address: ferroma@mcmaster.ca.

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

PMID:
24210708
DOI:
10.1016/j.annepidem.2013.10.007
[Indexed for MEDLINE]

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