Random survival forests for competing risks

Hemant Ishwaran, Thomas A. Gerds, Udaya B. Kogalur, Richard D. Moore, Stephen J. Gange, Bryan M. Lau

Research output: Contribution to journalArticle

67 Scopus citations

Abstract

We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection in high-dimensional problems and in settings such as HIV/AIDS that involve many competing risks.

Original languageEnglish (US)
Pages (from-to)757-773
Number of pages17
JournalBiostatistics
Volume15
Issue number4
DOIs
StatePublished - 2014

Keywords

  • AIDS
  • Brier score
  • C-index
  • Competing risks
  • Cumulative incidence function
  • Ensemble

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty

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  • Cite this

    Ishwaran, H., Gerds, T. A., Kogalur, U. B., Moore, R. D., Gange, S. J., & Lau, B. M. (2014). Random survival forests for competing risks. Biostatistics, 15(4), 757-773. https://doi.org/10.1093/biostatistics/kxu010