title: Bayesian inference for two-part mixed-effects model using skew distributions, with application to longitudinal semicontinuous alcohol data.
abstract: Semicontinuous data featured with an excessive proportion of zeros and right-skewed continuous positive values arise frequently in practice. One example would be the substance abuse/dependence symptoms data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyze repeated measures of semicontinuous data from longitudinal studies. In this paper, we propose a flexible two-part mixed-effects model with skew distributions for correlated semicontinuous alcohol data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: (i) a model on the occurrence of positive values using a generalized logistic mixed-effects model (Part I); and (ii) a model on the intensity of positive values using a linear mixed-effects model where the model errors follow skew distributions including skew- t and skew-normal distributions (Part II). The proposed method is illustrated with an alcohol abuse/dependence symptoms data from a longitudinal observational study, and the analytic results are reported by comparing potential models under different random-effects structures. Simulation studies are conducted to assess the performance of the proposed models and method.
authors.affiliation: 1 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, USA.
authors.forename: Dongyuan
authors.initials: D
authors.lastname: Xing
authors.affiliation: 1 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, USA.
authors.forename: Yangxin
authors.initials: Y
authors.lastname: Huang
authors.affiliation: 1 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, USA.
authors.forename: Henian
authors.initials: H
authors.lastname: Chen
authors.affiliation: 1 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, USA.
authors.forename: Yiliang
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: 1 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, USA.
authors.forename: Getachew A
authors.initials: GA
authors.lastname: Dagne
authors.affiliation: 2 Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, USA.
authors.forename: Julie
authors.initials: J
authors.lastname: Baldwin
mesh_terms: D000293:Adolescent; D000328:Adult; D000437:Alcoholism / Q000523:psychology*; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D009010:Monte Carlo Method; D009042:Motivation; D016011:Normal Distribution; D055815:Young Adult
keywords: Bayesian analysis; alcohol abuse/dependence symptoms data; semicontinuous data; skew distributions; two-part mixed-effects model