title: Random survival forests for competing risks.
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.
authors.affiliation: Division of Biostatistics, University of Miami, Miami, FL 33136, USA hemant.ishwaran@gmail.com.
authors.forename: Hemant
authors.initials: H
authors.lastname: Ishwaran
authors.affiliation: Department of Biostatistics, University of Copenhagen, 1014 Copenhagen, Denmark.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Gerds
authors.affiliation: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, USA.
authors.forename: Udaya B
authors.initials: UB
authors.lastname: Kogalur
authors.affiliation: Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: Richard D
authors.initials: RD
authors.lastname: Moore
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Gange
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: Bryan M
authors.initials: BM
authors.lastname: Lau
mesh_terms: D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000188:drug therapy / Q000401:mortality; D006801:Humans; D015233:Models, Statistical; D012306:Risk; D016019:Survival Analysis
keywords: AIDS; Brier score; C-index; Competing risks; Cumulative incidence function; Ensemble
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