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

BMC Medical Research Methodology

Jitske J. Sijbrandij¹  Josué Almansa¹  Tialda Hoekstra¹  Sijmen A. Reijneveld¹  Ute Bültmann¹  Margot Peeters² 

[1] Department of Health Sciences, Community and Occupational Medicine Groningen, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands;Department of Interdisciplinary Social Science, Utrecht University, Utrecht, Netherlands;

关键词: Simulation studies;    Longitudinal studies;    Developmental trajectories;    Growth mixture model;    Variance misspecification;    Model selection;   

DOI  :  10.1186/s12874-020-01154-0

来源: Springer

【 摘 要 】

BackgroundGrowth 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.MethodsTo 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.ResultsIf 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.ConclusionsIf 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.

【 授权许可】

CC BY