A semiparametric modeling approach using Bayesian additive regression trees with an application to evaluate heterogeneous treatment effects

DOI10.1214/19-AOAS1266zbMath1434.62231arXiv1806.04200OpenAlexW2980812053WikidataQ100727406 ScholiaQ100727406MaRDI QIDQ2281248

Vincent III Lo Re, Bret Zeldow, Jason A. Roy

Publication date: 19 December 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.04200

zbMATH Keywords

Bayesian additive regression treesantiretroviralsstructural mean model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (3)

Bayesian additive regression trees for genotype by environment interaction models ⋮ A semiparametric modeling approach using Bayesian additive regression trees with an application to evaluate heterogeneous treatment effects ⋮ semibart

Uses Software

• R
• mgcv
• BayesTree
• BayesDA
• BartPy

Cites Work

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• A semiparametric modeling approach using Bayesian additive regression trees with an application to evaluate heterogeneous treatment effects
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• Bayesian Analysis of Binary and Polychotomous Response Data
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