Boosting for high-dimensional time-to-event data with competing risks | Zendy

Harald Binder | Zendy; Arthur Allignol | Zendy; Martin Schumacher | Zendy; Jan Beyersmann | Zendy

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Boosting for high-dimensional time-to-event data with competing risks

Author(s) -

Harald Binder,

Arthur Allignol,

Martin Schumacher,

Jan Beyersmann

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp088

Subject(s) - boosting (machine learning) , covariate , censoring (clinical trials) , computer science , r package , data mining , event (particle physics) , proportional hazards model , statistics , machine learning , mathematics , physics , computational science , quantum mechanics

For analyzing high-dimensional time-to-event data with competing risks, tailored modeling techniques are required that consider the event of interest and the competing events at the same time, while also dealing with censoring. For low-dimensional settings, proportional hazards models for the subdistribution hazard have been proposed, but an adaptation for high-dimensional settings is missing. In addition, tools for judging the prediction performance of fitted models have to be provided.

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