IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v24y2018i1d10.1007_s10985-016-9387-7.html
   My bibliography  Save this article

Conditional screening for ultra-high dimensional covariates with survival outcomes

Author

Listed:
  • Hyokyoung G. Hong

    (Michigan State University)

  • Jian Kang

    (University of Michigan)

  • Yi Li

    (University of Michigan)

Abstract

Identifying important biomarkers that are predictive for cancer patients’ prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorily known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a diffuse large B-cell lymphoma dataset. We are pleased to dedicate this work to Jack Kalbfleisch, who has made instrumental contributions to the development of modern methods of analyzing survival data.

Suggested Citation

  • Hyokyoung G. Hong & Jian Kang & Yi Li, 2018. "Conditional screening for ultra-high dimensional covariates with survival outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 45-71, January.
  • Handle: RePEc:spr:lifeda:v:24:y:2018:i:1:d:10.1007_s10985-016-9387-7
    DOI: 10.1007/s10985-016-9387-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-016-9387-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10985-016-9387-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Emre Barut & Jianqing Fan & Anneleen Verhasselt, 2016. "Conditional Sure Independence Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1266-1277, July.
    2. Zhao, Sihai Dave & Li, Yi, 2012. "Principled sure independence screening for Cox models with ultra-high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 397-411.
    3. Rui Song & Wenbin Lu & Shuangge Ma & X. Jessie Jeng, 2014. "Censored rank independence screening for high-dimensional survival data," Biometrika, Biometrika Trust, vol. 101(4), pages 799-814.
    4. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Douglas E. Schaubel & Bin Nan, 2018. "Special issue dedicated to Jack Kalbfleisch," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 1-2, January.
    2. Jing Pan & Yuan Yu & Yong Zhou, 2018. "Nonparametric independence feature screening for ultrahigh-dimensional survival data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(7), pages 821-847, October.
    3. Zhang, Shen & Zhao, Peixin & Li, Gaorong & Xu, Wangli, 2019. "Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 37-52.
    4. Ke, Chenlu & Yang, Wei & Yuan, Qingcong & Li, Lu, 2023. "Partial sufficient variable screening with categorical controls," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    5. Qu, Lianqiang & Wang, Xiaoyu & Sun, Liuquan, 2022. "Variable screening for varying coefficient models with ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    6. Jing Zhang & Haibo Zhou & Yanyan Liu & Jianwen Cai, 2021. "Conditional screening for ultrahigh-dimensional survival data in case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 632-661, October.
    7. Chen, Xiaolin & Zhang, Yahui & Chen, Xiaojing & Liu, Yi, 2019. "A simple model-free survival conditional feature screening," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 156-160.
    8. Eun Ryung Lee & Seyoung Park & Sang Kyu Lee & Hyokyoung G. Hong, 2023. "Quantile forward regression for high-dimensional survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 769-806, October.
    9. Hong, Hyokyoung G. & Zheng, Qi & Li, Yi, 2019. "Forward regression for Cox models with high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 268-290.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Zhang & Haibo Zhou & Yanyan Liu & Jianwen Cai, 2021. "Conditional screening for ultrahigh-dimensional survival data in case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 632-661, October.
    2. Jing Pan & Yuan Yu & Yong Zhou, 2018. "Nonparametric independence feature screening for ultrahigh-dimensional survival data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(7), pages 821-847, October.
    3. Jing Zhang & Qihua Wang & Xuan Wang, 2022. "Surrogate-variable-based model-free feature screening for survival data under the general censoring mechanism," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 379-397, April.
    4. Grace Y. Yi & Wenqing He & Raymond. J. Carroll, 2022. "Feature screening with large‐scale and high‐dimensional survival data," Biometrics, The International Biometric Society, vol. 78(3), pages 894-907, September.
    5. Hyokyoung G. Hong & Xuerong Chen & David C. Christiani & Yi Li, 2018. "Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes," Biometrics, The International Biometric Society, vol. 74(2), pages 421-429, June.
    6. Xuewei Cheng & Gang Li & Hong Wang, 2024. "The concordance filter: an adaptive model-free feature screening procedure," Computational Statistics, Springer, vol. 39(5), pages 2413-2436, July.
    7. Chen, Xiaolin & Chen, Xiaojing & Wang, Hong, 2018. "Robust feature screening for ultra-high dimensional right censored data via distance correlation," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 118-138.
    8. Zhong, Wei & Wang, Jiping & Chen, Xiaolin, 2021. "Censored mean variance sure independence screening for ultrahigh dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    9. Hong, Hyokyoung G. & Zheng, Qi & Li, Yi, 2019. "Forward regression for Cox models with high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 268-290.
    10. Jialiang Li & Qi Zheng & Limin Peng & Zhipeng Huang, 2016. "Survival impact index and ultrahigh‐dimensional model‐free screening with survival outcomes," Biometrics, The International Biometric Society, vol. 72(4), pages 1145-1154, December.
    11. Jing Zhang & Guosheng Yin & Yanyan Liu & Yuanshan Wu, 2018. "Censored cumulative residual independent screening for ultrahigh-dimensional survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(2), pages 273-292, April.
    12. Tian, Bing & Liu, Zili & Wang, Hong, 2022. "Non-marginal feature screening for varying coefficient competing risks model," Statistics & Probability Letters, Elsevier, vol. 190(C).
    13. Jinfeng Xu & Wai Keung Li & Zhiliang Ying, 2020. "Variable screening for survival data in the presence of heterogeneous censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1171-1191, December.
    14. Jing Zhang & Yanyan Liu & Hengjian Cui, 2021. "Model-free feature screening via distance correlation for ultrahigh dimensional survival data," Statistical Papers, Springer, vol. 62(6), pages 2711-2738, December.
    15. Yang Qu & Yu Cheng, 2023. "Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 735-751, October.
    16. Jing Zhang & Haibo Zhou & Yanyan Liu & Jianwen Cai, 2021. "Feature screening for case‐cohort studies with failure time outcome," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 349-370, March.
    17. Zhang, Jing & Liu, Yanyan & Wu, Yuanshan, 2017. "Correlation rank screening for ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 121-132.
    18. Jianglin Fang, 2021. "Feature screening for ultrahigh-dimensional survival data when failure indicators are missing at random," Statistical Papers, Springer, vol. 62(3), pages 1141-1166, June.
    19. Pan, Yingli, 2022. "Feature screening and FDR control with knockoff features for ultrahigh-dimensional right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    20. Zhang, Shen & Zhao, Peixin & Li, Gaorong & Xu, Wangli, 2019. "Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 37-52.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lifeda:v:24:y:2018:i:1:d:10.1007_s10985-016-9387-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.