ArticleStatistics in medicine2019
Bayesian additive regression trees and the General BART model.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Blood Pressure Targets for Adults with Vasodilatory Shock - An Individual Patient Data Meta-Analysis.NEJM evidence · 2025Pooled it
- Uncovering hypothesis-generating associations between prescription drugs and digestive cancer risk: a case-control drug-wide association study (DWAS).Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes With Ordinal Outcomes.Statistics in medicine · 2026Article
- Regional variability in the associations between social and health-related risk factors and memory across Europe.Scientific reports · 2026Article
- Multi-Level Variable Selection Using a BART-Enhanced Mixed-Effects Framework.Statistics in medicine · 2026Article
- A hierarchical conformal framework for uncertainty-aware length of stay prediction in multi-hospital settings.Scientific reports · 2026Article
- Flexible statistical approaches for modeling nonlinear relationships in diabetes prediction using splines, Bayesian kernel regression and Bayesian regression trees.Discover analytics · 2026Article
- A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes with application to a cardiovascular disease cohort study.Biostatistics (Oxford, England) · 2025Article
- BARTSIMP: Flexible spatial covariate modeling and prediction using Bayesian Additive Regression Trees.Spatial and spatio-temporal epidemiology · 2025Article
- Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Bayesian additive regression trees for machine learning to classify benign vs atypical lipomatous tumors on MRI.Radiology advances · 2025Article
- Quantifying the uncertainty of human activity recognition using a Bayesian machine learning method: a prediction study.Physical activity and nutrition · 2025Article
- Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment.Scientific reports · 2025Article
- A Bayesian Approach to the G-Formula via Iterative Conditional Regression.Statistics in medicine · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Bayesian Additive Regression Trees for Group Testing Data.Statistics in medicine · 2025Article
- Long-term effect of pharmacological treatment on academic achievement of Norwegian children diagnosed with ADHD: a target trial emulation.International journal of epidemiology · 2025Observational
- Predicting the Mechanical Properties of Supramolecular Gels.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Bayesian nonparametric trees for principal causal effects.Biometrics · 2025Article
- Leveraging Machine Learning and Real-World Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations.International journal of chronic obstructive pulmonary disease · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers through the details of BART, from what it is to why it works. This tutorial is aimed at providing such a resource. In addition to explaining the different components of BART using simple examples, we also discuss a framework, the General BART model that unifies some of the recent BART extensions, including semiparametric models, correlated outcomes, and statistical matching problems in surveys, and models with weaker distributional assumptions. By showing how these models fit into a single framework, we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.