Variance constraints strongly influenced model performance in growth mixture modeling: a simulation and empirical study

Jitske J Sijbrandij*, Tialda Hoekstra, Josué Almansa, Margot Peeters, Ute Bültmann, Sijmen A Reijneveld

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

BACKGROUND: Growth Mixture Modeling (GMM) is commonly used to group individuals on their development over time, but convergence issues and impossible values are common. This can result in unreliable model estimates. Constraining variance parameters across classes or over time can solve these issues, but can also seriously bias estimates if variances differ. We aimed to determine which variance parameters can best be constrained in Growth Mixture Modeling.

METHODS: To identify the variance constraints that lead to the best performance for different sample sizes, we conducted a simulation study and next verified our results with the TRacking Adolescent Individuals' Lives Survey (TRAILS) cohort.

RESULTS: If variance parameters differed across classes and over time, fitting a model without constraints led to the best results. No constrained model consistently performed well. However, the model that constrained the random effect variance and residual variances across classes consistently performed very poorly. For a small sample size (N = 100) all models showed issues. In TRAILS, the same model showed substantially different results from the other models and performed poorly in terms of model fit.

CONCLUSIONS: If possible, a Growth Mixture Model should be fit without any constraints on variance parameters. If not, we recommend to try different variance specifications and to not solely rely on the default model, which constrains random effect variances and residual variances across classes. The variance structure must always be reported Researchers should carefully follow the GRoLTS-Checklist when analyzing and reporting trajectory analyses.

Original languageEnglish
Article number276
Pages (from-to)1-15
Number of pages15
JournalBMC Medical Research Methodology
Volume20
Issue number1
DOIs
Publication statusPublished - 12-Nov-2020

Keywords

  • Simulation studies
  • Longitudinal studies
  • Developmental trajectories
  • Growth mixture model
  • Variance misspecification
  • Model selection
  • DEVELOPMENTAL TRAJECTORIES
  • IMPACT
  • CHILDHOOD
  • BEHAVIORS
  • TIME

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