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Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use

Authors
Lee, Jung WunChung, HwanJeon, Saebom
Issue Date
9월-2021
Publisher
ELSEVIER
Keywords
Adolescent depression; Label switching; Latent class analysis; Longitudinal data; Markov chain Monte Carlo; Substance use
Citation
COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.161
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume
161
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/136416
DOI
10.1016/j.csda.2021.107261
ISSN
0167-9473
Abstract
Multivariate latent class profile analysis (MLCPA) is a useful tool for exploring the stage-sequential process of multiple latent class variables, but the inference can be challenging due to the high-dimensional latent structure of the model. In this paper, a Bayesian approach via Markov chain Monte Carlo (MCMC) is proposed for MLCPA as an alternative to the maximum-likelihood (ML) method. Compared to the ML solution, Bayesian estimates are less sensitive to the set of initial values as well as easier to obtain standard errors. We also address issues in MCMC such as label-switching problem with a dynamic data-dependent prior and computational complexity with a recursive formula. Simulation studies revealed the validity and efficiency of the proposed algorithm. An empirical analysis of MLCPA using the National Longitudinal Survey of Youth 97 (NLSY97) identified a small number of representative developmental progressions of adolescent depression and substance use. (C) 2021 Published by Elsevier B.V.
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