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Penalized variable selection in competing risks regression

Author

Listed:
  • Zhixuan Fu

    (Yale University)

  • Chirag R. Parikh

    (Yale University)

  • Bingqing Zhou

    (Yale University
    Novartis AG)

Abstract

Penalized variable selection methods have been extensively studied for standard time-to-event data. Such methods cannot be directly applied when subjects are at risk of multiple mutually exclusive events, known as competing risks. The proportional subdistribution hazard (PSH) model proposed by Fine and Gray (J Am Stat Assoc 94:496–509, 1999) has become a popular semi-parametric model for time-to-event data with competing risks. It allows for direct assessment of covariate effects on the cumulative incidence function. In this paper, we propose a general penalized variable selection strategy that simultaneously handles variable selection and parameter estimation in the PSH model. We rigorously establish the asymptotic properties of the proposed penalized estimators and modify the coordinate descent algorithm for implementation. Simulation studies are conducted to demonstrate the good performance of the proposed method. Data from deceased donor kidney transplants from the United Network of Organ Sharing illustrate the utility of the proposed method.

Suggested Citation

  • Zhixuan Fu & Chirag R. Parikh & Bingqing Zhou, 2017. "Penalized variable selection in competing risks regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 353-376, July.
    • Handle: RePEc:spr:lifeda:v:23:y:2017:i:3:d:10.1007_s10985-016-9362-3
    DOI: 10.1007/s10985-016-9362-3
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    References listed on IDEAS

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    Cited by:

    1. Hui, Francis K.C. & Müller, Samuel & Welsh, A.H., 2020. "The LASSO on latent indices for regression modeling with ordinal categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    2. Lu, Shuiyun & Chen, Xiaolin & Xu, Sheng & Liu, Chunling, 2020. "Joint model-free feature screening for ultra-high dimensional semi-competing risks data," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    3. Erqian Li & Jianxin Pan & Manlai Tang & Keming Yu & Wolfgang Karl Härdle & Xiaowen Dai & Maozai Tian, 2023. "Weighted Competing Risks Quantile Regression Models and Variable Selection," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    4. Tian, Bing & Liu, Zili & Wang, Hong, 2022. "Non-marginal feature screening for varying coefficient competing risks model," Statistics & Probability Letters, Elsevier, vol. 190(C).

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