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

Abstract

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.

Transformed Gaussian Markov random fields and spatial modeling of species abundance

“…However, our modeling framework can be interpreted as a link function [39] with the dependence structure defined by the Gaussian copula, in such way that a multivariate distribution is defined for µ. For this reason, this modeling setup is not suitable for the LGM class since our representation does not have a latent field with additive distribution.…”

“…Both models can be viewed as hidden GMRF models: a GMRF is hidden behind the link functions in a SGLMM, whereas it is hidden behind the marginal quantile functions in a TGMRF. The quantile functions could be viewed as a class of new link functions [39]. Therefore, the computation burden and the convergence speed of the two models are very similar, as long as the quantile functions are not much more expensive to evaluate than the link functions.…”

Uma Abordagem Estatística para a Análise dos Resultados das Eleições Presidenciais

“…Portanto, em futuros trabalhos, é possível desenvolver abordagens alternativas para analisar a correlação entre as regiões (PRATES et al, 2022) a partir de uma perspectiva espaçotemporal, utilização de campos aleatórios de Markov (PRATES et al, 2015) para lidar com estruturas de dependência e a incorporação de efeitos aleatórios (PRATES et al, 2013).…”