Random survival forests for competing risks

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

doi:10.1093/biostatistics/kxu010

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Random survival forests for competing risks

Journal article

Open access

Peer reviewed

Random survival forests for competing risks

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

Biostatistics (Oxford, England), Vol.15(4), pp.757-773

2014-10

DOI: https://doi.org/10.1093/biostatistics/kxu010

PMCID: PMC4173102

PMID: 24728979

Abstract

Data Interpretation, Statistical

Humans

Survival Analysis

HIV Infections - drug therapy

HIV Infections - mortality

Risk

Models, Statistical

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.

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 9 Mathematics; 9.92 Statistical Methods; 9.92.220 Nonparametric Regression
Web Of Science research areas: Mathematical & Computational Biology; Statistics & Probability
ESI research areas: Mathematics

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Details

Title: Random survival forests for competing risks
Creators: Hemant Ishwaran - Division of Biostatistics, University of Miami, Miami, FL 33136, USA hemant.ishwaran@gmail.com
Thomas A Gerds - Department of Biostatistics, University of Copenhagen, 1014 Copenhagen, Denmark
Udaya B Kogalur - Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, USA
Richard D Moore - Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
Stephen J Gange - Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
Bryan M Lau - Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
Publication Details: Biostatistics (Oxford, England), Vol.15(4), pp.757-773
Publisher: England
Grant note: U01-AI42590 / NIAID NIH HHS R01CA16373 / NCI NIH HHS K01-AI071754 / NIAID NIH HHS U01 DA036935 / NIDA NIH HHS U01 AI069918 / NIAID NIH HHS R01-DA011602 / NIDA NIH HHS R01 CA163739 / NCI NIH HHS
Academic Unit: Miller School of Medicine; UMMG Department of Public Health Sciences Research
Language: English
Resource Type: Journal article
PMID: 24728979
PMCID: PMC4173102
Record Identifier: 991031561674502976