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Group and within-group variable selection for competing risks data

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  • Kwang Woo Ahn

    (Medical College of Wisconsin)

  • Anjishnu Banerjee

    (Medical College of Wisconsin)

  • Natasha Sahr

    (Medical College of Wisconsin)

  • Soyoung Kim

    (Medical College of Wisconsin)

Abstract

Variable selection in the presence of grouped variables is troublesome for competing risks data: while some recent methods deal with group selection only, simultaneous selection of both groups and within-group variables remains largely unexplored. In this context, we propose an adaptive group bridge method, enabling simultaneous selection both within and between groups, for competing risks data. The adaptive group bridge is applicable to independent and clustered data. It also allows the number of variables to diverge as the sample size increases. We show that our new method possesses excellent asymptotic properties, including variable selection consistency at group and within-group levels. We also show superior performance in simulated and real data sets over several competing approaches, including group bridge, adaptive group lasso, and AIC / BIC-based methods.

Suggested Citation

  • Kwang Woo Ahn & Anjishnu Banerjee & Natasha Sahr & Soyoung Kim, 2018. "Group and within-group variable selection for competing risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 407-424, July.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:3:d:10.1007_s10985-017-9400-9
    DOI: 10.1007/s10985-017-9400-9
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    References listed on IDEAS

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    1. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    2. Brent R. Logan & Mei-Jie Zhang & John P. Klein, 2011. "Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues," Biometrics, The International Biometric Society, vol. 67(1), pages 1-7, March.
    3. Jianwen Cai & Jianqing Fan & Runze Li & Haibo Zhou, 2005. "Variable selection for multivariate failure time data," Biometrika, Biometrika Trust, vol. 92(2), pages 303-316, June.
    4. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    5. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. He, Yizeng & Kim, Soyoung & Kim, Mi-Ok & Saber, Wael & Ahn, Kwang Woo, 2021. "Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. 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.

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