Hidden Markov models for zero-inflated Poisson counts with an application to substance use

Stacia M. Desantis, Dipankar Bandyopadhyay

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1678-1694
Number of pages17
JournalStatistics in Medicine
Issue number14
StatePublished - Jun 30 2011
Externally publishedYes


  • Bayesian
  • Cue-reactivity
  • Hidden Markov model
  • Markov chain Monte Carlo
  • Zero inflation


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