Hidden Markov models for alcoholism treatment trial data
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ID: 282116
2010
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Abstract
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.
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oslin2010hidden
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| Authors | Kenneth E. Shirley; Dylan S. Small; Kevin G. Lynch; Stephen A. Maisto; David W. Oslin |
| Journal | arXiv |
| Year | 2010 |
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