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Penalized Variable Selection for Multi-center Competing Risks Data

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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.

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Acknowledgments

Thank Dr. Schaubel for the invitation of submission. This publication was made possible by CTSA Grant Number UL1 TR000142 from the National Center for Advancing Translational Science (NCATS), a component of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH. This work was supported in part by Health Resources and Services Administration contract 234-2005-37011C. The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.

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Authors and Affiliations

  1. Biostatistics Department, Yale University, 60 College Street, New Haven, CT, 06510, USA

    Zhixuan Fu, Shuangge Ma & Haiqun Lin

  2. Section of Nephrology, Department of Internal Medicine, Yale University, 60 Temple Street, Suite 6C, New Haven, CT, 06510, USA

    Chirag R. Parikh

  3. Celgene Corporation, 86 Morris Ave, Summit, NJ, 07901, USA

    Bingqing Zhou

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  1. Zhixuan Fu
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  2. Shuangge Ma
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Correspondence to Bingqing Zhou.

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Fu, Z., Ma, S., Lin, H. et al. Penalized Variable Selection for Multi-center Competing Risks Data. Stat Biosci 9, 379–405 (2017). https://doi.org/10.1007/s12561-016-9181-9

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