Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package

Alcohol use disorder (AUD) research faces significant challenges in capturing individual heterogeneity and complex temporal patterns in drinking behaviors. Standard statistical methods fail to account for within-person variability and between-person differences, while existing machine learning algorithms are not designed for hierarchical, longitudinal data structures common in AUD research. We developed a comprehensive R package implementing 30 Bayesian machine learning functions specifically designed for alcohol use research, spanning interpretable linear and logistic regression to flexible Bayesian additive regression trees (BART), all with mixed-effects and time-trend extensions. We demonstrate the package capabilities using alcohol use data from two studies: the ABQDrinQ longitudinal cohort (n = 190) and the COMBINE clinical trial (n = 1,383). Key findings include strong associations between concurrent substance use and alcohol consumption (nicotine use associated with 13.5% increase in drinks and 14 times higher odds of drinking), discovery of nonlinear age effects on drinking variability (peak at ages 25-30), and high-accuracy daily predictions (median correlation 0.82, median absolute error 1.0 drinks). The Bayesian framework provides uncertainty quantification essential for both research and clinical applications, while the range of algorithms allows researchers to navigate the complexity-interpretability tradeoff. While developed for alcohol research, the methodological framework addresses statistical challenges common across substance use research.