Estimating heterogeneous survival treatment effect in observational data using machine learning-Reference-Cited by-同舟云学术

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Estimating heterogeneous survival treatment effect in observational data using machine learning

Published:2021-06-10 Issue:21 Volume:40 Page:4691-4713
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Hu Liangyuan¹²^ORCID,Ji Jiayi¹,Li Fan³⁴^ORCID

Affiliation:

1. Department of Population Health Science and Policy Icahn School of Medicine at Mount Sinai New York NY USA

2. Department of Biostatistics and Epidemiology Rutgers School of Public Health Piscataway NJ USA

3. Department of Biostatistics Yale University School of Public Health New Haven Connecticut USA

4. Center for Methods in Implementation and Prevention Science Yale University School of Public Health New Haven Connecticut USA

Funder

National Cancer Institute

Patient-Centered Outcomes Research Institute

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9090

Reference63 articles.

1. Subgroup identification from randomized clinical trial data

2. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

3. Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

4. Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data

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1. Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment;Medical Decision Making;2024-07-26

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4. Contrast-specific propensity scores for causal inference with multiple interventions;Statistical Methods in Medical Research;2024-03-18

5. A Bayesian machine learning approach for estimating heterogeneous survivor causal effects: Applications to a critical care trial;The Annals of Applied Statistics;2024-03-01

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