Mixture models for clustering multilevel growth trajectories

Authors:
S. K. Ng;G. J. Mclachlan
Affiliations:
School of Medicine, Griffith Health Institute, Griffith University, Australia;Department of Mathematics, University of Queensland, Australia
Venue:
Computational Statistics & Data Analysis
Year:
2014

Citing 3
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Abstract

Mixture model-based methods assuming independence may not be valid for clustering growth trajectories arising from multilevel studies because longitudinal data collected from the same unit are often correlated. A mixture of mixed effects models is considered to capture the correlation using multilevel and multivariate random effects. Furthermore, the mixing proportions are allowed to depend on covariates. The additional information is thus incorporated into the mixture model to adjust for individual probabilities of membership of the components. The proposed method is illustrated using simulated and real multilevel growth trajectory data sets from various scientific fields.