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Survival impact index and ultrahigh‐dimensional model‐free screening with survival outcomes

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  • Jialiang Li
  • Qi Zheng
  • Limin Peng
  • Zhipeng Huang

Abstract

Motivated by ultrahigh‐dimensional biomarkers screening studies, we propose a model‐free screening approach tailored to censored lifetime outcomes. Our proposal is built upon the introduction of a new measure, survival impact index (SII). By its design, SII sensibly captures the overall influence of a covariate on the outcome distribution, and can be estimated with familiar nonparametric procedures that do not require smoothing and are readily adaptable to handle lifetime outcomes under various censoring and truncation mechanisms. We provide large sample distributional results that facilitate the inference on SII in classical multivariate settings. More importantly, we investigate SII as an effective screener for ultrahigh‐dimensional data, not relying on rigid regression model assumptions for real applications. We establish the sure screening property of the proposed SII‐based screener. Extensive numerical studies are carried out to assess the performance of our method compared with other existing screening methods. A lung cancer microarray data is analyzed to demonstrate the practical utility of our proposals.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1145-1154
    DOI: 10.1111/biom.12499
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    References listed on IDEAS

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    Cited by:

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    6. 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.
    7. 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.
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    9. 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.
    10. 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.

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