ArticleStatistical methods in medical research2020
Nonparametric competing risks analysis using Bayesian Additive Regression Trees.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The BIOPREVENT machine-learning algorithm predicts chronic graft-versus-host disease and mortality risk using posttransplant biomarkers.The Journal of clinical investigation · 2026Trial
- Article
- 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
- Blood proteomics for quantitative biomarkers of cellular therapies.Biomarker research · 2025Review
- Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization.Journal of translational medicine · 2024Article
- Article
- Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.NPJ precision oncology · 2023Article
- Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles.Genetic epidemiology · 2023Article
- Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning.JCO clinical cancer informatics · 2021Article
- Biomarkers for Allogeneic HCT Outcomes.Frontiers in immunology · 2020Review
- Bayesian additive regression trees and the General BART model.Statistics in medicine · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many time-to-event studies are complicated by the presence of competing risks. Such data are often analyzed using Cox models for the cause-specific hazard function or Fine and Gray models for the subdistribution hazard. In practice, regression relationships in competing risks data are often complex and may include nonlinear functions of covariates, interactions, high-dimensional parameter spaces and nonproportional cause-specific, or subdistribution, hazards. Model misspecification can lead to poor predictive performance. To address these issues, we propose a novel approach: flexible prediction modeling of competing risks data using Bayesian Additive Regression Trees (BART). We study the simulation performance in two-sample scenarios as well as a complex regression setting, and benchmark its performance against standard regression techniques as well as random survival forests. We illustrate the use of the proposed method on a recently published study of patients undergoing hematopoietic stem cell transplantation.
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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.