ArticleBiostatistics (Oxford, England)2020
Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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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.
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Who cites it
32 citing papers in PubMed, 4 citations in OpenAlex.
- Applying methods for personalized medicine to the treatment of alcohol use disorder.Journal of consulting and clinical psychology · 2021Trial
- Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes.Lifetime data analysis · 2026Article
- Individualised treatment effect prediction: the benefit of initial biological treatment in early rheumatoid arthritis.EULAR rheumatology open · 2025Article
- Bayesian Nonparametric Models for Multiple Raters: A General Statistical Framework.Psychometrika · 2025Article
- TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.The annals of applied statistics · 2025Article
- A monotone single index model for spatially referenced multistate current status data.Biometrics · 2025Article
- Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.Lifetime data analysis · 2025Article
- Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 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
- Evaluating Meta-Learners to Analyze Treatment Heterogeneity in Survival Data: Application to Electronic Health Records of Pediatric Asthma Care in COVID-19 Pandemic.Statistics in medicine · 2025Article
- Heterogeneous Mediation Analysis for Cox Proportional Hazards Model With Multiple Mediators.Statistics in medicine · 2024Article
- A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes.Journal of data science : JDS · 2024Article
- Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Review
- Optimal Donor Selection Across Multiple Outcomes For Hematopoietic Stem Cell Transplantation By Bayesian Nonparametric Machine Learning.medRxiv : the preprint server for health sciences · 2024Article
- A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.The annals of applied statistics · 2024Article
- Heterogeneous treatment effect estimation for observational data using model-based forests.Statistical methods in medical research · 2024Article
- A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes.Lifetime data analysis · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Individuals often respond differently to identical treatments, and characterizing such variability in treatment response is an important aim in the practice of personalized medicine. In this article, we describe a nonparametric accelerated failure time model that can be used to analyze heterogeneous treatment effects (HTE) when patient outcomes are time-to-event. By utilizing Bayesian additive regression trees and a mean-constrained Dirichlet process mixture model, our approach offers a flexible model for the regression function while placing few restrictions on the baseline hazard. Our nonparametric method leads to natural estimates of individual treatment effect and has the flexibility to address many major goals of HTE assessment. Moreover, our method requires little user input in terms of model specification for treatment covariate interactions or for tuning parameter selection. Our procedure shows strong predictive performance while also exhibiting good frequentist properties in terms of parameter coverage and mitigation of spurious findings of HTE. We illustrate the merits of our proposed approach with a detailed analysis of two large clinical trials (N = 6769) for the prevention and treatment of congestive heart failure using an angiotensin-converting enzyme inhibitor. The analysis revealed considerable evidence for the presence of HTE in both trials as demonstrated by substantial estimated variation in treatment effect and by high proportions of patients exhibiting strong evidence of having treatment effects which differ from the overall treatment effect.
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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.