Regularized outcome weighted subgroup identification for differential treatment effects-Reference-Cited by-同舟云学术

Regularized outcome weighted subgroup identification for differential treatment effects

Published:2015-05-11 Issue:3 Volume:71 Page:645-653
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biom

Author:

Xu Yaoyao¹,Yu Menggang²,Zhao Ying-Qi²,Li Quefeng³,Wang Sijian¹²,Shao Jun¹

Affiliation:

1. Department of Statistics; University of Wisconsin; Madison, Wisconsin U.S.A.

2. Department of Biostatistics & Medical Informatics; University of Wisconsin; Madison, Wisconsin U.S.A.

3. Department of Operation Research and Financial Engineering; Princeton University; Princeton, New Jersey U.S.A.

Funder

National Institute for Health Research

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/biom.12322/fullpdf

Reference37 articles.

1. Convexity, classification, and risk bounds;Bartlett;Journal of the American Statistical Association,2006

2. Resampling fewer than n observations: Gains, losses, and remedies for losses;Bickel;Statistica Sinica,1997

3. Subgroup analyses in randomized trials: Risks of subgroup-specific analyses; power and sample size for the interaction test;Brookes;Journal of Clinical Epidemiology,2004

4. Analysis of randomized comparative clinical trial data for personalized treatment selections;Cai;Biostatistics,2011

Cited by 60 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. 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

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

3. Finding the best subgroup with differential treatment effect with multiple outcomes;Statistics in Medicine;2024-04-15

4. A Selective Review of Individualized Decision Making;ICSA Book Series in Statistics;2024

5. Iterative Causal Forest: A Novel Algorithm for Subgroup Identification;American Journal of Epidemiology;2023-11-07