A Multi-Level Two-Part Random Effects Model, with Application to an Alcohol Dependence Study
Keywords: Hierarchical model; Longitudinal data analysis; Mixed model; Logistic model; Nested random effects; Generalized linear mixed model
Two-part random effects models (Olsen and Schafer 2001; Tooze, Grunwald, and Jones 2002) have been applied to longitudinal studies for semi-continuous outcomes, characterized by a large portion of zero values and continuous non zero (positive) values. Examples include repeated measures of daily drinking records and monthly medical costs. However, it remains a question to apply such models to multi-level data settings. In this paper we propose a novel multi-level two-part random effects model. Distinct random effects are used to characterize heterogeneity at different levels. MLE and inference are carried out through Gaussian quadrature technique, which can be implemented conveniently in freely available software - aML. The model is applied to the analysis of repeated measures of the daily drinking record in a randomized controlled trial of topiramate for alcohol-dependence treatment.