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

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 journal › Article › peer-review

87 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 language	English (US)
Pages (from-to)	757-773
Number of pages	17
Journal	Biostatistics
Volume	15
Issue number	4
DOIs	https://doi.org/10.1093/biostatistics/kxu010
State	Published - Oct 1 2014

Keywords

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

ASJC Scopus subject areas

General Medicine

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10.1093/biostatistics/kxu010

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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.",

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author = "Hemant Ishwaran and Gerds, {Thomas A.} and Kogalur, {Udaya B.} and Moore, {Richard D.} and Gange, {Stephen J.} and Lau, {Bryan M.}",

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AU - Kogalur, Udaya B.

AU - Moore, Richard D.

AU - Gange, Stephen J.

AU - Lau, Bryan M.

PY - 2014/10/1

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AB - 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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KW - Brier score

KW - C-index

KW - Competing risks

KW - Cumulative incidence function

KW - Ensemble

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Random survival forests for competing risks

Abstract

Keywords

ASJC Scopus subject areas

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