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

Dongyuan Xing, Yangxin Huang, Henian Chen, Yiliang Zhu, Getachew A. Dagne, Julie Baldwin

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1838-1853
Number of pages16
JournalStatistical Methods in Medical Research
Volume26
Issue number4
DOIs
StatePublished - Aug 1 2017
Externally publishedYes

Keywords

  • alcohol abuse/dependence symptoms data
  • Bayesian analysis
  • semicontinuous data
  • skew distributions
  • two-part mixed-effects model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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