Bayesian fusion forests for heterogeneous treatment effects on survival from randomised and real-world data
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Abstract (arXiv)
We develop the Bayesian fusion forest, a nonparametric framework to estimate heterogeneous treatment effects on survival outcomes by combining a randomised controlled trial and real-world data. The framework relaxes the unconfoundedness assumption on the real-world data by assuming instead that the treatment effect transports across the two sources. Our method opens up right- and interval-censored outcomes to data fusion. We model the survival time with an accelerated failure time decomposition into a shared baseline prognosis, a source-specific deviation, a treatment effect, and a confounding function. The confounding function absorbs the confounding bias in the real-world data. Each component receives a Bayesian tree ensemble prior. The shared baseline prognosis borrows strength across sources, while the deviation captures between-source heterogeneity. A hierarchical Dirichlet process mixture models the error distribution nonparametrically. A simulation study shows efficiency gains over a trial-only analysis across varying levels of confounding and between-source heterogeneity. We combine the ACTG 175 trial with the Multicenter AIDS Cohort Study to estimate the effect of combination antiretroviral therapy for HIV. The fusion identifies a benefit for nearly every patient whereas the trial alone is inconclusive.
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References · 23
- 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
- 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
- 2018Removing Hidden Confounding by Experimental Groundingcited by 64
- 2021Semiparametric counterfactual density estimationcited by 42
- 2017Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectscited by 41
- 2020Survival regression with accelerated failure time model in XGBoostcited by 37
- 2013bartMachine: Machine Learning with Bayesian Additive Regression Treescited by 24
- 2017Generalizing causal inferences from individuals in randomized trials to all trial-eligible individualscited by 24
- 2020Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimationcited by 17
- 2017Individualized Treatment Effects with Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Modelscited by 13
- 2020Data fusion methods for the heterogeneity of treatment effect and confounding functioncited by 9
- 2019Model interpretation through lower-dimensional posterior summarizationcited by 5
11 more
- 2023Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learningcited by 4
- 2020A flexible parametric accelerated failure time modelcited by 3
- 2021Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression Treescited by 2
- 2024Causal-ICM: A Data Fusion Framework For Heterogeneous Treatment Effect Estimation With Multi-Task Gaussian Processescited by 2
- 2025Statistical Inference for Heterogeneous Treatment Effect with Right-censored Data from Synthesizing Randomized Clinical Trials and Real-world Datacited by 2
- 2024The causal interpretation of acceleration factorscited by 1
- 2025Integrative Analysis of High-dimensional RCT and RWD Subject to Censoring and Hidden Confoundingcited by 1
- 2025Quantifying uncertainty of individualized treatment effects in right-censored survival data: A comparison of Bayesian additive regression trees and causal survival forestcited by 1
- 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
- 2025Counterfactual Survival Q-learning via Buckley-James Boosting, with Applications to ACTG 175 and CALGB 8923cited by 1
- 2026ShrinkageTrees: An R Package for Bayesian Tree Ensembles for Survival Analysis and Causal Inferencecited by 1
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