Bayesian zero-inflated growth mixture models with application to health risk behavior data

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Date of Original Version



This paper focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete longitudinal patterns of rare events counts (for instance, health-risky behavior), and to identify individual-specific covariates associated with latent class probabilities. Two discrete latent structures are present in this type of model: a latent categorical variable that classifies subgroups with distinct developmental trajectories and a latent binary variable that identifies whether an observation is from a zero-inflation process or a regular count process. Within each class, two sets of covariates are used to separately model the probability of structural zeros and the mean trajectories of the count process. The estimation of the latent variables and regression parameters are carried jointly in a hierarchical Bayesian framework. Our methods are validated through a simulation study and then applied to cigarette smoking data, obtained from the National Longitudinal Study of Adolescent Health.

Publication Title, e.g., Journal

Statistics and its Interface