Estimating Individualized Treatment Rules Using Outcome Weighted Learning-Reference-Cited by-同舟云学术

Estimating Individualized Treatment Rules Using Outcome Weighted Learning

Published:2012-06-04 Issue:499 Volume:107 Page:1106-1118
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Zhao Yingqi¹,Zeng Donglin¹,Rush A. John²,Kosorok Michael R.³

Affiliation:

1. a Department of Biostatistics , University of North Carolina , Chapel Hill , NC , 27599

2. b Office of Clinical Sciences , Duke-National University of Singapore Graduate Medical School , Singapore , 169857

3. c Department of Biostatistics, and Professor, Department of Statistics and Operations Research , University of North Carolina at Chapel Hill , Chapel Hill , NC , 27599

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2012.695674

Reference41 articles.

1. Local Rademacher complexities

2. Convexity, Classification, and Risk Bounds

3. Statistical performance of support vector machines

4. Role of biologic therapy and chemotherapy in hormone receptor- and HER2-positive breast cancer

Cited by 478 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. Leveraging artificial intelligence and machine learning analytics for development effectiveness: a comprehensive approach to extract insights from project data;Journal of Development Effectiveness;2024-08-23

2. Data Science Methods for Real-World Evidence Generation in Real-World Data;Annual Review of Biomedical Data Science;2024-08-23

3. Measuring the individualization potential of treatment individualization rules: Application to rules built with a new parametric interaction model for parallel-group clinical trials;Statistical Methods in Medical Research;2024-08-06

4. Bayesian shrinkage estimation of credible subgroups for count data with excess zeros;Statistics in Biopharmaceutical Research;2024-08-05

5. Learning decision catalogues for situated decision making: The case of scoring systems;International Journal of Approximate Reasoning;2024-08