HIDDEN MARKOV MODELS FOR ALCOHOLISM TREATMENT TRIAL DATA
University of Pennsylvania and Syracuse University


In a clinical trial of a treatment for alcoholism, the usual response variable of interest is the number of alcoholic drinks consumed by each subject each day. Subjects in these trials are typically volunteers who are alcoholics, and thus are prone to erratic drinking behaviors, 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 a Hidden Markov model (HMM) for categorical data, where the counts of drinks per day are summarized as a categorical variable with three levels, as is the convention in alcohol research.

We compare the HMM's properties to those of other models, focusing on out-of-sample prediction error as well as interpretability; to do this, we analyze data from a clinical trial of the drug Naltrexone using each model.

The HMM performs at a level comparable to the other models with respect to out-of-sample prediction error, and contains unique features that allow for useful clinical interpretations.

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