期刊
STATISTICS IN MEDICINE
卷 30, 期 14, 页码 1678-1694出版社
WILEY
DOI: 10.1002/sim.4207
关键词
Bayesian; cue-reactivity; hidden Markov model; Markov chain Monte Carlo; zero inflation
类别
资金
- U.S. National Institutes of Health [5U10-DA013727-09, P20-RR017696-06]
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. Copyright (C) 2011 John Wiley & Sons, Ltd.
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