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Assessing intervention efficacy on high‐risk drinkers using generalized linear mixed models with a new class of link functions
Author(s) -
Prates Marcos O.,
Aseltine Robert H.,
Dey Dipak K.,
Yan Jun
Publication year - 2013
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201300015
Subject(s) - overdispersion , generalized linear mixed model , generalized linear model , intervention (counseling) , random effects model , bayesian probability , generalized additive model , predictive power , class (philosophy) , count data , statistics , model selection , econometrics , computer science , mathematics , medicine , meta analysis , artificial intelligence , poisson distribution , philosophy , epistemology , psychiatry
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.