Assessing intervention efficacy on high-risk drinkers using generalized linear mixed models with a new class of link functions

Unhealthy alcohol use is one of the leading causes of morbidity and mortality in the United States. Brief interventions with high‐risk drinkers during an emergency department (ED) visit are of great interest due to their possible efficacy and low cost. In a collaborative study with patients recruite...

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Bibliographic Details
Published in:Biometrical journal 2013-11, Vol.55 (6), p.912-924
Main Authors: Prates, Marcos O., Aseltine Jr, Robert H., Dey, Dipak K., Yan, Jun
Format: Article
Language:English
Subjects:
Alcohol Drinking - prevention & control
Bayesian analysis
Bayesian method
Count data
Economic models
Emergency Service, Hospital
Humans
Intervention
Linear Models
MCMC
Models, Statistical
Mortality
Overdispersion
Poisson Distribution
Regression Analysis
Risk
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Summary:Unhealthy alcohol use is one of the leading causes of morbidity and mortality in the United States. Brief interventions with high‐risk drinkers during an emergency department (ED) visit are of great interest due to their possible efficacy and low cost. In a collaborative study with patients recruited at 14 academic ED across the United States, we examined the self‐reported number of drinks per week by each patient following the exposure to a brief intervention. Count data with overdispersion have been mostly analyzed with generalized linear mixed models (GLMMs), of which only a limited number of link functions are available. Different choices of link function provide different fit and predictive power for a particular dataset. We propose a class of link functions from an alternative way to incorporate random effects in a GLMM, which encompasses many existing link functions as special cases. The methodology is naturally implemented in a Bayesian framework, with competing links selected with Bayesian model selection criteria such as the conditional predictive ordinate (CPO). In application to the ED intervention study, all models suggest that the intervention was effective in reducing the number of drinks, but some new models are found to significantly outperform the traditional model as measured by CPO. The validity of CPO in link selection is confirmed in a simulation study that shared the same characteristics as the count data from high‐risk drinkers. The dataset and the source code for the best fitting model are available in Supporting Information.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.201300015