Bayesian inference for two-part mixed-effects model using skew distributions, with application to longitudinal semicontinuous alcohol data

• Published in: Statistical methods in medical research Vol. 26; no. 4; p. 1838
• Main Authors: Xing, Dongyuan, Huang, Yangxin, Chen, Henian, Zhu, Yiliang, Dagne, Getachew A, Baldwin, Julie
• Format: Journal Article
• Language: English
• Published: England 01.08.2017
• Subjects: Adolescent; Adult; Alcoholism - psychology; Bayes Theorem; Female; Humans; Linear Models; Longitudinal Studies; Male; Markov Chains; Monte Carlo Method; Motivation; Normal Distribution; Young Adult; semicontinuous data; two-part mixed-effects model; alcohol abuse/dependence symptoms data; Bayesian analysis; skew distributions
• ISSN: 1477-0334
• DOI: 10.1177/0962280215590284

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.