Variance constraints strongly influenced model performance in growth mixture modeling: a simulation and empirical study
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(2020) 20:276
RESEARCH ARTICLE
Open Access
Variance constraints strongly influenced model performance in growth mixture modeling: a simulation and empirical study Jitske J. Sijbrandij1* , Tialda Hoekstra1, Josué Almansa1, Margot Peeters2, Ute Bültmann1 and Sijmen A. Reijneveld1
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. Keywords: Simulation studies, Longitudinal studies, Developmental trajectories, Growth mixture model, Variance misspecification, Model selection
* Correspondence: [email protected] 1 Department of Health Sciences, Community and Occupational Medicine Groningen, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulatio
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