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.
Boosting; Competing risks; High-dimensional data; Random survival forest; Stability; Variable selection;
J. Gilhodes, C. Zemmour, S. Ajana, A. Martinez, J.P Delord, Eve Leconte, Jean Marie Boher, and Thomas Filleron, “Comparison of variable selection methods for high-dimensional survival data with competing events”, Computers in Biology and Medicine, vol. 91, n. 1, December 2017, pp. 159–167.
Computers in Biology and Medicine, vol. 91, n. 1, December 2017, pp. 159–167