Bayesian nonparametric latent class model for longitudinal data.

Statistical Methods in Medical Research
Wonmo Koo, Heeyoung Kim

Abstract

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.

References

Jan 23, 2008·Biostatistics·Martyn Plummer
May 13, 2009·Biometrics·Yisheng LiPeter Müller
Jul 14, 2011·Statistics in Medicine·Brian NeelonMarie Lynn Miranda
Apr 17, 2012·Journal of the American Statistical Association·Hongxia YangDavid B Dunson
Jun 22, 2013·American Journal of Epidemiology·Ellen B GoldSiobán D Harlow
Dec 12, 2013·Psychometrika·Jia-Chiun Pan, Guan-Hua Huang
Jan 1, 2016·Journal of the American Statistical Association·Fernando A QuintanaEllen Gold

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