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Journal of the American Statistical Association Volume 115, 2020 - Issue 530
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Theory and Methods

Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes

Zhengling Qia Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC; View further author information
,
Dacheng Liub Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT; View further author information
,
Haoda Fuc Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN; View further author information
&
Yufeng Liud Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NCCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-1686-0545View further author information
ORCID Icon
Pages 678-691 | Received 10 Sep 2017, Accepted 16 Sep 2018, Published online: 11 Apr 2019

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Canhui Li, Weirong Li & Wensheng Zhu. (2024) Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects. Journal of Applied Statistics 51:6, pages 1151-1170.
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Chengchun Shi, Shikai Luo, Yuan Le, Hongtu Zhu & Rui Song. (2024) Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons. Journal of the American Statistical Association 119:545, pages 232-245.
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Zhengling Qi, Jong-Shi Pang & Yufeng Liu. (2023) On Robustness of Individualized Decision Rules. Journal of the American Statistical Association 118:543, pages 2143-2157.
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Fei Xue, Yanqing Zhang, Wenzhuo Zhou, Haoda Fu & Annie Qu. (2022) Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data. Journal of the American Statistical Association 117:539, pages 1438-1451.
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Weibin Mo, Zhengling Qi & Yufeng Liu. (2021) Learning Optimal Distributionally Robust Individualized Treatment Rules. Journal of the American Statistical Association 116:534, pages 659-674.
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Yifan Cui & Eric Tchetgen Tchetgen. (2021) A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity. Journal of the American Statistical Association 116:533, pages 162-173.
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Steve Yadlowsky, Fabio Pellegrini, Federica Lionetto, Stefan Braune & Lu Tian. (2021) Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data. Journal of the American Statistical Association 116:533, pages 335-352.
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Yaoming Zhen & Junhui Wang. (2024) Nonnegative tensor completion for dynamic counterfactual prediction on COVID-19 pandemic. The Annals of Applied Statistics 18:1.
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Qi Xu, Haoda Fu & Annie Qu. (2024) Optimal individualized treatment rule for combination treatments under budget constraints. Journal of the Royal Statistical Society Series B: Statistical Methodology.
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Qi Xu, Xiaoke Cao, Geping Chen, Hanqi Zeng, Haoda Fu & Annie Qu. (2024) Multi-label residual weighted learning for individualized combination treatment rule. Electronic Journal of Statistics 18:1.
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Jacob M. Maronge, Jared D. Huling & Guanhua Chen. (2023) A reluctant additive model framework for interpretable nonlinear individualized treatment rules. The Annals of Applied Statistics 17:4.
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Kushal S. Shah, Haoda Fu & Michael R. Kosorok. (2023) Stabilized Direct Learning for Efficient Estimation of Individualized Treatment Rules. Biometrics 79:4, pages 2843-2856.
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Zhishuai Liu, Zishu Zhan, Cunjie Lin & Baqun Zhang. (2023) Estimation in optimal treatment regimes based on mean residual lifetimes with right‐censored data. Biometrical Journal 65:8.
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Larry Dong, Erica E. M. Moodie, Laura Villain & Rodolphe Thiébaut. (2023) Evaluating the use of generalized dynamic weighted ordinary least squares for individualized HIV treatment strategies. The Annals of Applied Statistics 17:3.
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Xiwen Jiang & Weining Shen. (2023) Simultaneous Denoising and Heterogeneity Learning for Time Series Data. Statistics in Biosciences.
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Junyi Zhou, Ying Zhang & Wanzhu Tu. (2022) A reference-free R-learner for treatment recommendation. Statistical Methods in Medical Research 32:2, pages 404-424.
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Luo Li, Richard A. Levine & Juanjuan Fan. (2022) Causal effect random forest of interaction trees for learning individualized treatment regimes with multiple treatments in observational studies. Stat 11:1.
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Shiferaw B Bizuayehu & Jin Xu. (2022) Model-free screening for variables with treatment interaction. Statistical Methods in Medical Research 31:10, pages 1845-1859.
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Yiwei Fan, Xiaoling Lu, Junlong Zhao, Haoda Fu & Yufeng Liu. (2022) Estimating individualized treatment rules for treatments with hierarchical structure. Electronic Journal of Statistics 16:1.
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Pierre Bunouf, Mélanie Groc, Alex Dmitrienko & Ilya Lipkovich. (2021) Data-Driven Subgroup Identification in Confirmatory Clinical Trials. Therapeutic Innovation & Regulatory Science 56:1, pages 65-75.
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Sewoong Hwang, Yungyeong Song & Jonghyuk Kim. (2021) Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application. Applied Sciences 11:6, pages 2707.
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Pingye Zhang, Junshui Ma, Xinqun Chen & Yue Shentu. (2020) A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data. Statistics in Medicine 39:28, pages 4133-4146.
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