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Lifetime Data Analysis

, Volume 24, Issue 3, pp 407–424 | Cite as

Group and within-group variable selection for competing risks data

  • Kwang Woo Ahn
  • Anjishnu Banerjee
  • Natasha Sahr
  • Soyoung Kim
Article

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.

Keywords

Adaptive penalty Clustered data Competing risks data Group bridge 

Notes

Acknowledgements

The US National Cancer Institute (U24CA076518) partially supported this work. The authors would like to thank the Associate Editor and two reviewers for their helpful comments that significantly improved the paper.

Supplementary material

10985_2017_9400_MOESM1_ESM.pdf (59 kb)
Supplementary Materials The proofs of Theorem 2 and Corollary 1 and a list of variables for the bone marrow transplant data are available online. (PDF 60KB).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Division of BiostatisticsMedical College of WisconsinMilwaukeeUSA

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