Individualized Treatment Effect Prediction with Machine Learning — Salient Considerations-Reference-Cited by-同舟云学术

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Individualized Treatment Effect Prediction with Machine Learning — Salient Considerations

Published:2024-03-26 Issue:4 Volume:3 Page:
ISSN:2766-5526
Container-title:NEJM Evidence
language:en
Short-container-title:NEJM Evidence

Author:

Desai Rishi J.¹,Glynn Robert J.¹,Solomon Scott D.²,Claggett Brian²,Wang Shirley V.¹,Vaduganathan Muthiah²

Affiliation:

1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston

2. Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston

Publisher

Massachusetts Medical Society

Reference31 articles.

1. Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence

2. Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses

3. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects

4. A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research

5. Metalearners for estimating heterogeneous treatment effects using machine learning

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Letter Re: Real-world outcomes of Italian patients with advanced non-squamous lung cancer treated with first-line pembrolizumab plus platinum-pemetrexed;European Journal of Cancer;2024-08

2. Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes;American Society of Clinical Oncology Educational Book;2024-06

3. Heterogeneity of Treatment Effect — An Evolution in Subgroup Analysis;NEJM Evidence;2024-03-26

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