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An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis

Author

Listed:
  • Kevin He

    (University of Michigan)

  • Yue Wang

    (University of Michigan)

  • Xiang Zhou

    (University of Michigan)

  • Han Xu

    (University of Michigan)

  • Can Huang

    (University of Michigan)

Abstract

Motivated by high-dimensional genomic studies, we develop an improved procedure for adaptive Lasso in high-dimensional survival analysis. The proposed procedure effectively reduces the false discoveries while successfully maintaining the false negative proportions, which improves the existing adaptive Lasso procedures. The implementation of the proposed procedure is straightforward and it is sufficiently flexible to accommodate large-scale problems where traditional procedures are impractical. To quantify the uncertainty of variable selection and control the family-wise error rate, a multiple sample-splitting based testing algorithm is developed. The practical utility of the proposed procedure are examined through simulation studies. The methods developed are then applied to a multiple myeloma data set.

Suggested Citation

  • Kevin He & Yue Wang & Xiang Zhou & Han Xu & Can Huang, 2019. "An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 569-585, July.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:3:d:10.1007_s10985-018-9455-2
    DOI: 10.1007/s10985-018-9455-2
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    References listed on IDEAS

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