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High-Dimensional Variable Selection for Survival Data

Author

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  • Ishwaran, Hemant
  • Kogalur, Udaya B.
  • Gorodeski, Eiran Z.
  • Minn, Andy J.
  • Lauer, Michael S.

Abstract

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Suggested Citation

  • Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
    • Handle: RePEc:bes:jnlasa:v:105:i:489:y:2010:p:205-217
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    1. Xiajing Gong & Meng Hu & Jinzhong Liu & Geoffrey Kim & James Xu & Amy McKee & Todd Palmby & R. Angelo Claro & Liang Zhao, 2022. "Decoding kinase-adverse event associations for small molecule kinase inhibitors," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Hoora Moradian & Denis Larocque & François Bellavance, 2017. "$$L_1$$ L 1 splitting rules in survival forests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 671-691, October.
    3. Demir Djekic & Erika Fagman & Oskar Angerås & George Lappas & Kjell Torén & Göran Bergström & Annika Rosengren, 2020. "Social Support and Subclinical Coronary Artery Disease in Middle-Aged Men and Women: Findings from the Pilot of Swedish CArdioPulmonary bioImage Study," IJERPH, MDPI, vol. 17(3), pages 1-16, January.
    4. Rossella Tatoli & Luisa Lampignano & Rossella Donghia & Alfredo Niro & Fabio Castellana & Ilaria Bortone & Roberta Zupo & Sarah Tirelli & Madia Lozupone & Francesco Panza & Giovanni Alessio & Francesc, 2023. "Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    5. Eiran Z Gorodeski & Emer Joyce & Benjamin T Gandesbery & Eugene H Blackstone & David O Taylor & W H Wilson Tang & Randall C Starling & Rory Hachamovitch, 2017. "Discordance between 'actual' and 'scheduled' check-in times at a heart failure clinic," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
    6. Ismael Ahrazem Dfuf & José Manuel Mira McWilliams & María Camino González Fernández, 2019. "Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis," Energies, MDPI, vol. 12(6), pages 1-24, March.
    7. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
    8. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
    9. Shang-Ming Zhou & Fabiola Fernandez-Gutierrez & Jonathan Kennedy & Roxanne Cooksey & Mark Atkinson & Spiros Denaxas & Stefan Siebert & William G Dixon & Terence W O’Neill & Ernest Choy & Cathie Sudlow, 2016. "Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
    10. Yiwei Fan & Gang Wang & Xiaoling Lu & Gaobin Wang, 2019. "Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-26, December.
    11. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," LSE Research Online Documents on Economics 111529, London School of Economics and Political Science, LSE Library.
    12. Yuan, Ao & Xu, Jinfeng & Zheng, Gang, 2012. "Root-n estimability of some missing data models," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 147-166.
    13. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 140-162.
    14. Ran Dai & Cheng Zheng & Mei-Jie Zhang, 2023. "On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 242-260, April.
    15. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    16. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    17. Christine Porzelius & Martin Schumacher & Harald Binder, 2011. "The benefit of data-based model complexity selection via prediction error curves in time-to-event data," Computational Statistics, Springer, vol. 26(2), pages 293-302, June.
    18. Foucher Yohann & Danger Richard, 2012. "Time Dependent ROC Curves for the Estimation of True Prognostic Capacity of Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(6), pages 1-22, November.
    19. Peter Calhoun & Melodie J. Hallett & Xiaogang Su & Guy Cafri & Richard A. Levine & Juanjuan Fan, 2020. "Random forest with acceptance–rejection trees," Computational Statistics, Springer, vol. 35(3), pages 983-999, September.
    20. Mao, Xiaojun & Peng, Liuhua & Wang, Zhonglei, 2022. "Nonparametric feature selection by random forests and deep neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    21. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.
    22. J. Choi & S. Ye & K. H. Eng & K. Korthauer & W. H. Bradley & J. S. Rader & C. Kendziorski, 2017. "IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 1-12, June.

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