Analysis of Extreme Drinking in Patients with Alcohol Dependence Using Pareto Regression
1SAMSI and Duke University
2Department of Statistics, University of Connecticut, Storrs, CT
3Department of Psychiatry, University of Connecticut Health Center, CT
October 23, 2008
We developed a novel Pareto regression model with unknown shape parameter to analyze extreme drinking in patients with Alcohol Dependence (AD). We used a generalized linear models (GLM) framework and a log-link between the shape parameter of the random and systematic components and a Monte Carlo based Bayesian method to implement the analysis. We examined two issues of importance in the study of AD: First, we tested whether a single nucleotide polymorphism within GABRA2 gene, which encodes a subunit of the GABAA receptor and has been associated to AD, influenced extreme alcohol intake and second, the efficacy of three psychotherapies for alcoholism in treating extreme drinking behavior. European-American participants (n = 812, 73.4% male) from Project MATCH, a multi-center randomized clinical trial of the psychotherapeutic treatment of alcoholism, provided DNA samples for this study. Following 3-month treatment period, during which patients received one of the three-psychotherapy treatment, participants were followed up at 3 month intervals. We found that women with the high-risk GABRA2 allele had a significantly higher probability of extreme drinking behavior than women with no high-risk allele. High-risk women also responded to therapy better than those with two low-risk alleles. We found that women who received cognitive behaviorial therapy had better outcomes than those those receiving either of the other two therapies. Among men, there was no significant effect of GABRA2 genotype on extreme drinking behavior. However, motivational enhancement therapy was the best treatment for the extreme drinking behavior.
Keywords: Alcohol Dependence, Bayesian Modeling, Extreme Behavior, GABRA2, Jeffreys' prior, Pareto Regression.