Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials-Reference-Cited by-同舟云学术

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Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials

Published:2022-11-27 Issue: Volume: Page:
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biometrical J

Author:

Sun Sophie¹,Sechidis Konstantinos²,Chen Yao¹,Lu Jiarui¹,Ma Chong³,Mirshani Ardalan¹,Ohlssen David¹,Vandemeulebroecke Marc⁴,Bornkamp Björn²^ORCID

Affiliation:

1. Advanced Methodology and Data Science Novartis Pharmaceuticals Corporation East Hanover New Jersey USA

2. Advanced Methodology and Data Science Novartis Pharma AG Basel Switzerland

3. Early Development Analytics Novartis Pharmaceuticals Corporation Cambridge Massachusetts USA

4. Clinical Development and Analytics Novartis Pharma AG Basel Switzerland

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100337

Reference75 articles.

1. A comparative study of subgroup identification methods for differential treatment effect: Performance metrics and recommendations;Alemayehu D.;Statistical Methods in Medical Research,2018

2. Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials;Alosh M.;Statistics in Medicine,2017

3. Recursive partitioning for heterogeneous causal effects;Athey S.;Proceedings of the National Academy of Sciences,2016

4. Generalized random forests;Athey S.;The Annals of Statistics,2019

5. Estimating treatment effects with causal forests: An application;Athey S.;Observational Studies,2019

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2. Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data;Statistics in Medicine;2024-07-25

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