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

Abstract

MOTIVATION 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… (More)
DOI: 10.1093/bioinformatics/btp088

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