Concordance-assisted learning for estimating optimal individualized treatment regimes-Reference-Cited by-同舟云学术

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Concordance-assisted learning for estimating optimal individualized treatment regimes

Published:2016-10-31 Issue:5 Volume:79 Page:1565-1582
ISSN:1369-7412
Container-title:Journal of the Royal Statistical Society: Series B (Statistical Methodology)
language:en
Short-container-title:J. R. Stat. Soc. B

Author:

Fan Caiyun¹,Lu Wenbin²,Song Rui²,Zhou Yong³

Affiliation:

1. Shanghai University of International Business and Economics; People's Republic of China

2. North Carolina State University; Raleigh USA

3. Shanghai University of Finance and Economics and Chinese Academy of Sciences; Beijing People's Republic of China

Funder

National Institutes of Health

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/rssb.12216/fullpdf

Reference27 articles.

1. Pairwise-difference rank estimation of the transformation model;Abrevaya;J. Bus. Econ. Statist.,2003

2. Blatt , D. Murphy , S. Zhu , J. 2004 A-learning for approximate planning Technical Report 04-63

3. Standard errors and covariance matrices for smoothed rank estimators;Brown;Biometrika,2005

4. Induced smoothing for rank regression with censored survival times;Brown;Statist. Med.,2007

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1. Learning Non-monotone Optimal Individualized Treatment Regimes;Statistica Sinica;2024

2. Significance test for semiparametric conditional average treatment effects and other structural functions;Computational Statistics & Data Analysis;2024-01

3. Adaptive Semi-supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data;ICSA Book Series in Statistics;2024

4. A reluctant additive model framework for interpretable nonlinear individualized treatment rules;The Annals of Applied Statistics;2023-12-01

5. Estimating heterogeneous treatment effects with right-censored data via causal survival forests;Journal of the Royal Statistical Society Series B: Statistical Methodology;2023-02-27

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