Hidden Markov models for alcoholism treatment trial data

Kenneth E. Shirley, Dylan S. Small, Kevin G. Lynch, Stephen A. Maisto, David W. Oslin

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version of this response, with levels corresponding to abstinence, light drinking and heavy drinking. In these trials, within-subject drinking patterns are often characterized by alternating periods of heavy drinking and abstinence. For this reason, many statistical models for time series that assume steady behavior over time and white noise errors do not fit alcohol data well. In this paper we propose to describe subjects' drinking behavior using Markov models and hidden Markov models (HMMs), which are better suited to describe processes that make sudden, rather than gradual, changes over time. We incorporate random effects into these models using a hierarchical Bayes structure to account for correlated responses within subjects over time, and we estimate the effects of covariates, including a randomized treatment, on the outcome in a novel way. We illustrate the models by fitting them to a large data set from a clinical trial of the drug Naltrexone. The HMM, in particular, fits this data well and also contains unique features that allow for useful clinical interpretations of alcohol consumption behavior.

Original languageEnglish (US)
Pages (from-to)366-395
Number of pages30
JournalAnnals of Applied Statistics
Issue number1
StatePublished - Mar 2012


  • Alcohol relapse
  • Alcoholism clinical trial
  • Hidden Markov models
  • Longitudinal data
  • MCMC for mixture models
  • Mixed effects models

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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