Bayesian nonparametric latent class model for longitudinal data

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Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women's Health Across the Nation.
Publisher
SAGE PUBLICATIONS LTD
Issue Date
2020-11
Language
English
Article Type
Article
Citation

STATISTICAL METHODS IN MEDICAL RESEARCH, v.29, no.11, pp.3381 - 3395

ISSN
0962-2802
DOI
10.1177/0962280220928384
URI
http://hdl.handle.net/10203/276542
Appears in Collection
IE-Journal Papers(저널논문)
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