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Open Access Research article

A cautionary note regarding count models of alcohol consumption in randomized controlled trials

Nicholas J Horton1*, Eugenia Kim1 and Richard Saitz234

Author Affiliations

1 Department of Mathematics and Statistics, Smith College, Northampton, MA, USA

2 Clinical Addiction Research and Education (CARE) Unit, Section of General Internal Medicine, Boston Medical Center and Boston University School of Medicine, Boston, MA, USA

3 Youth Alcohol Prevention Center, Boston University School of Public Health, Boston, MA, USA

4 Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA

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BMC Medical Research Methodology 2007, 7:9  doi:10.1186/1471-2288-7-9

Published: 15 February 2007



Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies. The distribution of alcohol consumption is highly skewed, particularly in subjects with alcohol dependence.


In this paper, we will consider the use of count models for outcomes in a randomized clinical trial setting. These include the Poisson, over-dispersed Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial. We compare the Type-I error rate of these methods in a series of simulation studies of a randomized clinical trial, and apply the methods to the ASAP (Addressing the Spectrum of Alcohol Problems) trial.


Standard Poisson models provide a poor fit for alcohol consumption data from our motivating example, and did not preserve Type-I error rates for the randomized group comparison when the true distribution was over-dispersed Poisson. For the ASAP trial, where the distribution of alcohol consumption featured extensive over-dispersion, there was little indication of significant randomization group differences, except when the standard Poisson model was fit.


As with any analysis, it is important to choose appropriate statistical models. In simulation studies and in the motivating example, the standard Poisson was not robust when fit to over-dispersed count data, and did not maintain the appropriate Type-I error rate. To appropriately model alcohol consumption, more flexible count models should be routinely employed.