A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915411038044160 |
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| author | Solano, Pamela Van Horn, M Lee Walters, Kyle Besendorfer, Philipp Kuhlemeier, Alena Martínez-Ramón, Manel Jaki, Thomas |
| author_facet | Solano, Pamela Van Horn, M Lee Walters, Kyle Besendorfer, Philipp Kuhlemeier, Alena Martínez-Ramón, Manel Jaki, Thomas |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_19848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials Solano, Pamela Van Horn, M Lee Walters, Kyle Besendorfer, Philipp Kuhlemeier, Alena Martínez-Ramón, Manel Jaki, Thomas Applications 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. |
| title | A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials |
| topic | Applications |
| url | https://arxiv.org/abs/2507.19848 |