Challenges in modelling the random structure correctly in growth mixture models and the impact this has on model mixtures. (3rd March 2014)

• Record Type: Journal Article
• Authors: Gilthorpe, M. S.; Dahly, D. L.; Tu, Y.-K.; Kubzansky, L. D.; Goodman, E.
• Is Part Of: Journal of developmental origins of health and disease. Volume 5:Number 3(2014)
• Page Start: 197
• Page End: 205
• Publication Date: 2014-03-03
• Subjects: Developmental biology -- Periodicals; Embryology, Human -- Periodicals; Disease susceptibility -- Periodicals; Prenatal influences -- Periodicals; Diseases -- Causes and theories of causation -- Periodicals; 612.64
• DOI: 10.1017/S2040174414000130
• Languages: English
• ISSNs: 2040-1744

Abstract

<abstract abstract-type="normal"> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <p>Lifecourse trajectories of clinical or anthropological attributes are useful for identifying how our early-life experiences influence later-life morbidity and mortality. Researchers often use growth mixture models (GMMs) to estimate such phenomena. It is common to place constrains on the random part of the GMM to improve parsimony or to aid convergence, but this can lead to an autoregressive structure that distorts the nature of the mixtures and subsequent model interpretation. This is especially true if changes in the outcome within individuals are gradual compared with the magnitude of differences between individuals. This is not widely appreciated, nor is its impact well understood. Using repeat measures of body mass index (BMI) for 1528 US adolescents, we estimated GMMs that required variance–covariance constraints to attain convergence. We contrasted constrained models with and without an autocorrelation structure to assess the impact this had on the ideal number of latent classes, their size and composition. We also contrasted model options using simulations. When the GMM variance–covariance structure was constrained, a within-class autocorrelation structure emerged. When not modelled explicitly, this led to poorer model fit and models that differed substantially in the ideal number of latent classes, as well as class size and composition. Failure to carefully consider the random structure of data within a GMM framework may lead to erroneous model inferences, especially for outcomes with greater within-person than between-person homogeneity, such as BMI. It is crucial to reflect on the underlying data generation processes when building such models.</p> </abstract>