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Variable screening for survival data in the presence of heterogeneous censoring

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Listed:
  • Jinfeng Xu
  • Wai Keung Li
  • Zhiliang Ying

Abstract

Variable screening for censored survival data is most challenging when both survival and censoring times are correlated with an ultrahigh‐dimensional vector of covariates. Existing approaches to handling censoring often make use of inverse probability weighting by assuming independent censoring with both survival time and covariates. This is a convenient but rather restrictive assumption which may be unmet in real applications, especially when the censoring mechanism is complex and the number of covariates is large. To accommodate heterogeneous (covariate‐dependent) censoring that is often present in high‐dimensional survival data, we propose a Gehan‐type rank screening method to select features that are relevant to the survival time. The method is invariant to monotone transformations of the response and of the predictors, and works robustly for a general class of survival models. We establish the sure screening property of the proposed methodology. Simulation studies and a lymphoma data analysis demonstrate its favorable performance and practical utility.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:scjsta:v:47:y:2020:i:4:p:1171-1191
    DOI: 10.1111/sjos.12458
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    References listed on IDEAS

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    1. 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.

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