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Correlation rank screening for ultrahigh-dimensional survival data

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  • Zhang, Jing
  • Liu, Yanyan
  • Wu, Yuanshan

Abstract

With the recent explosion of ultrahigh-dimensional data, extensive work has been carried out for screening methods which can effectively reduce the dimensionality. However, censored survival data which often arise in clinical trials and genetic studies have been left greatly unexplored for ultrahigh-dimensional scenarios. A novel feature screening procedure is proposed for ultrahigh-dimensional survival data. Also established are the ranking consistency and the sure independent screening properties. Compared with the existing methods, the proposed screening procedure is invariant to the monotone transformation, known or unknown, of the response. Moreover, it can be readily applied to ultrahigh-dimensional complete data when the censoring rate is zero. Simulation studies demonstrate that the proposed procedure exhibits favorably in comparisons with the existing ones. As an illustration, the proposed method is applied to the mantle cell lymphoma study.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:108:y:2017:i:c:p:121-132
    DOI: 10.1016/j.csda.2016.11.005
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    References listed on IDEAS

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    Citations

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    Cited by:

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    3. 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.
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    5. Xiaolin Chen & Catherine Chunling Liu & Sheng Xu, 2021. "An efficient algorithm for joint feature screening in ultrahigh-dimensional Cox’s model," Computational Statistics, Springer, vol. 36(2), pages 885-910, June.
    6. Dominic Edelmann & Thomas Welchowski & Axel Benner, 2022. "A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing," Biometrics, The International Biometric Society, vol. 78(3), pages 867-879, September.
    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. 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.
    9. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    10. Xiaolin Chen & Yi Liu & Qihua Wang, 2019. "Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1007-1031, October.
    11. 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).
    12. 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.

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