A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials

TL;DR

This paper introduces HOBZ-BART, a Bayesian nonparametric model designed to accurately predict individual treatment effects for bounded semicontinuous outcomes in alcohol use disorder trials, improving personalized treatment strategies.

Contribution

HOBZ-BART is a novel Bayesian additive regression trees model that effectively models zero-one inflated data and estimates personalized treatment effects with interpretability and computational efficiency.

Findings

HOBZ-BART outperforms traditional models in predictive accuracy.

The model provides clinically meaningful treatment comparisons.

It enables personalized treatment insights in addiction research.

Abstract

Alcohol Use Disorder (AUD) treatment presents high individual-level heterogeneity, with outcomes ranging from complete abstinence to persistent heavy drinking. This variability-driven by complex behavioral, social, and environmental factors-poses major challenges for treatment evaluation and individualized decision-making. In particular, accurately modeling bounded semicontinuous outcomes and estimating predictive individual treatment effects (PITEs) remains methodologically demanding. For the pre-registered PITE analysis of Project MATCH, we developed HOBZ-BART, a novel Bayesian nonparametric model tailored for semicontinuous outcomes concentrated at clinically meaningful boundary values (0 and 1). The model decomposes the outcome into three components-abstinence, partial drinking, and persistent use-via a sequential hurdle structure, offering interpretability aligned with clinical reasoning. A shared Bayesian Additive Regression Tree (BART) ensemble captures nonlinear effects and covariate interactions across components, while a scalable Beta-likelihood approximation enables efficient, conjugate-friendly posterior computation. Through extensive simulations we demonstrate that HOBZ-BART outperforms traditional zero-one inflated Beta (ZOIB) model in predictive accuracy, computational efficiency, and PITE estimation. We then present the primary PITE analysis of the MATCH trial using HOBZ-BART which enables clinically meaningful comparisons of Cognitive Behavioral Therapy (CBT), Motivational Enhancement Therapy (MET), and Twelve Step Facilitation (TSF), offering personalized treatment insights. HOBZ-BART combines statistical rigor with clinical interpretability, addressing a critical need in addiction research for models that support individualized, data-driven care.