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Comparison of variable selection methods for high-dimensional survival data with competing events

Gilhodes, J., Zemmour, C., Ajana, S., Martinez, A., Delord, J.P, Leconte, Eve, Boher, Jean Marie and Filleron, Thomas (2017) Comparison of variable selection methods for high-dimensional survival data with competing events. Computers in Biology and Medicine, 91 (1). pp. 159-167.

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Official URL: http://tse-fr.eu/pub/32464

Abstract

In the era of personalized medicine, it's primordial to identify gene signatures for each event type in the context of competing risks in order to improve risk stratification and treatment strategy. Until recently, little attention was paid to the performance of high-dimensional selection in deriving molecular signatures in this context. In this paper, we investigate the performance of two selection methods developed in the framework of high-dimensional data and competing risks: Random survival forest and a boosting approach for fitting proportional subdistribution hazards models.

Item Type: Article
Language: English
Date: December 2017
Refereed: Yes
Uncontrolled Keywords: Boosting, Competing risks, High-dimensional data, Random survival forest, Stability, Variable selection
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 13 Apr 2018 14:54
Last Modified: 13 Apr 2018 14:54
OAI ID: oai:tse-fr.eu:32464
URI: http://publications.ut-capitole.fr/id/eprint/25847

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