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Penalized Variable Selection for Multi-center Competing Risks Data

Author

Listed:
  • Zhixuan Fu

    (Yale University)

  • Shuangge Ma

    (Yale University)

  • Haiqun Lin

    (Yale University)

  • Chirag R. Parikh

    (Yale University)

  • Bingqing Zhou

    (Celgene Corporation)

Abstract

We consider variable selection in competing risks regression for multi-center data. Our research is motivated by deceased donor kidney transplants, from which recipients would experience graft failure, death with functioning graft (DWFG), or graft survival. The occurrence of DWFG precludes graft failure from happening and therefore is a competing risk. Data within a transplant center may be correlated due to a latent center effect, such as varying patient populations, surgical techniques, and patient management. The proportional subdistribution hazard (PSH) model has been frequently used in the regression analysis of competing risks data. Two of its extensions, the stratified and the marginal PSH models, can be applied to multi-center data to account for the center effect. In this paper, we propose penalization strategies for the two models, primarily to select important variables and estimate their effects whereas correlations within centers serve as a nuisance. Simulations demonstrate good performance and computational efficiency for the proposed methods. It is further assessed using an analysis of data from the United Network of Organ Sharing.

Suggested Citation

  • Zhixuan Fu & Shuangge Ma & Haiqun Lin & Chirag R. Parikh & Bingqing Zhou, 2017. "Penalized Variable Selection for Multi-center Competing Risks Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 379-405, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9181-9
    DOI: 10.1007/s12561-016-9181-9
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    References listed on IDEAS

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    Cited by:

    1. 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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    3. 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).

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