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Model-X Knockoffs for high-dimensional controlled variable selection under the proportional hazards model with heterogeneity parameter

Author

Listed:
  • Ran Hu

    (Wuhan University)

  • Di Xia

    (Hubei University)

  • Haoyu Wang

    (Hubei University)

  • Caixu Xu

    (Guangxi Key Laboratory of Machine Vision and Intelligent Control)

  • Yingli Pan

    (Guangxi Key Laboratory of Machine Vision and Intelligent Control
    Hubei University)

Abstract

A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis can lead to the biased estimation. In this paper, regression analysis of the proportional hazards model with heterogeneity parameter is studied. We combine the Model-X Knockoffs procedure with fused LASSO approach to control the false discovery rate in the variable selection and learn the integrative data analysis of partially heterogeneous subgroups when the outcome of interest is time to event. A regularized working partial likelihood function is established and a trick of reparameterization is developed for the numerical calculation of the proposed estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. A data example from a clinical trial in primary biliary cirrhosis study is analyzed to demonstrate the application of our proposed method.

Suggested Citation

  • Ran Hu & Di Xia & Haoyu Wang & Caixu Xu & Yingli Pan, 2025. "Model-X Knockoffs for high-dimensional controlled variable selection under the proportional hazards model with heterogeneity parameter," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(4), pages 501-521, May.
    • Handle: RePEc:spr:metrik:v:88:y:2025:i:4:d:10.1007_s00184-024-00966-0
    DOI: 10.1007/s00184-024-00966-0
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    References listed on IDEAS

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    1. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.
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