ArticleStatistics in medicine2021
Estimating heterogeneous survival treatment effect in observational data using machine learning.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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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.
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Who cites it
35 citing papers in PubMed.
- Effect of chemical treatment on the mechanical and thermal performance of flax fiber reinforced aluminium 6082 laminate: a machine learning-enhanced investigation.Scientific reports · 2026Article
- Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning.Scientific reports · 2026Article
- Uncovering treatment effect heterogeneity in pragmatic gerontology trials.Experimental gerontology · 2026Article
- Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes.Lifetime data analysis · 2026Article
- Propensity score weighting analysis with complex survey data for estimating population-level treatment effects on survival: a simulation study.Health services & outcomes research methodology · 2025Article
- Unveiling non-small cell lung cancer treatment effect heterogeneity: a comparative analysis of statistical methods.Journal of the National Cancer Institute · 2025Article
- Pancancer outcome prediction via a unified weakly supervised deep learning model.Signal transduction and targeted therapy · 2025Article
- Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.Lifetime data analysis · 2025Article
- A Bayesian Approach to the G-Formula via Iterative Conditional Regression.Statistics in medicine · 2025Article
- AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times.Global epidemiology · 2025Article
- COMPARATIVE EFFECTIVENESS OF PROPENSITY SCORE ESTIMATION METHODS FOR INVERSE PROBABILITY OF TREATMENT WEIGHTING ANALYSIS WITH COMPLEX SURVEY DATA: A SIMULATION STUDY.Journal of survey statistics and methodology · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.Arabian journal of mathematics · 2025Article
- Modeling the Restricted Mean Survival Time Using Pseudo-Value Random Forests.Statistics in medicine · 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
- 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
- Propensity score weighting with survey weighted data when outcomes are binary: a simulation study.Health services & outcomes research methodology · 2024Article
- Heterogeneous treatment effect estimation for observational data using model-based forests.Statistical methods in medical research · 2024Article
- A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.The annals of applied statistics · 2024Article
- A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.Biometrical journal. Biometrische Zeitschrift · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of treatment effect heterogeneity and inform better practice, we carry out a comprehensive simulation study presenting a wide range of settings describing confounded heterogeneous survival treatment effects and varying degrees of covariate overlap. Our results suggest that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently yields the best performance, in terms of bias, precision, and expected regret. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Including a nonparametrically estimated propensity score as an additional fixed covariate in the AFT-BART-NP model formulation can further improve its efficiency and frequentist coverage. Finally, we demonstrate the application of flexible causal machine learning estimators through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer.
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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.