Abstract
BackgroundAlcohol 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.MethodsIn 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.ResultsStandard 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.ConclusionAs 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.
Highlights
Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies
One disadvantage of the Poisson is that it makes strong assumptions regarding the distribution of the underlying data. While these assumptions are tenable in some settings, they are less appropriate for alcohol consumption
Simulation studies In the simulation studies we assessed the behavior of models when the null hypothesis was true
Summary
Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies. Subjects may be queried about their daily consumption of alcohol, measured as a number of drinks over a recent period [1] (typically 30 days), and these values are used to estimate average drinking per day In this setting, estimating differences between treatment group and control group is of primary interest. A challenge in modeling consumption outcomes is to appropriately account for the distribution of drinking These distributions are characterized by a large number of zeros (abstinent subjects) along with a long right tail (page number not for citation purposes). One disadvantage of the Poisson is that it makes strong assumptions regarding the distribution of the underlying data (in particular, that the mean equals the variance) While these assumptions are tenable in some settings, they are less appropriate for alcohol consumption. Extensions of the Poisson, such as the over-dispersed Poisson, negative binomial and two stage (hurdle) or zero inflated models have been proposed [25]
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