title: Hidden Markov models for zero-inflated Poisson counts with an application to substance use.
abstract: Paradigms for substance abuse cue-reactivity research involve pharmacological or stressful stimulation designed to elicit stress and craving responses in cocaine-dependent subjects. It is unclear as to whether stress induced from participation in such studies increases drug-seeking behavior. We propose a 2-state Hidden Markov model to model the number of cocaine abuses per week before and after participation in a stress-and cue-reactivity study. The hypothesized latent state corresponds to 'high' or 'low' use. To account for a preponderance of zeros, we assume a zero-inflated Poisson model for the count data. Transition probabilities depend on the prior week's state, fixed demographic variables, and time-varying covariates. We adopt a Bayesian approach to model fitting, and use the conditional predictive ordinate statistic to demonstrate that the zero-inflated Poisson hidden Markov model outperforms other models for longitudinal count data.
authors.affiliation: Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. desantis@musc.edu
authors.forename: Stacia M
authors.initials: SM
authors.lastname: DeSantis
authors.forename: Dipankar
authors.initials: D
authors.lastname: Bandyopadhyay
mesh_terms: D000367:Age Factors; D000465:Algorithms; D001499:Bayes Theorem; D035841:Behavioral Research / Q000379:methods*; D019970:Cocaine-Related Disorders / Q000453:epidemiology*; D003198:Computer Simulation; D003463:Cues; D058999:Drug-Seeking Behavior; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution; D012008:Recurrence; D012044:Regression Analysis; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000453:epidemiology; D013315:Stress, Psychological / Q000139:chemically induced / Q000523:psychology