Cited by 2,714Statistical Methods and InferenceProbability and Statistical ResearchArtificial Intelligence in Healthcare

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Random survival forests

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Ishwaran, Hemant, Kogalur, Udaya B., Blackstone, Eugene H., Lauer, Michael S.·2008·The Annals of Applied Statistics

DOI 10.1214/08-aoas169

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Abstract

We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.

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Authors: Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S.
ORCID: Lauer, Michael S.
Venue: The Annals of Applied Statistics
Publisher: Institute of Mathematical Statistics
Published: 2008-09-01
Volume: 2
Issue: 3
Pages: 841-860
ISSN: 1932-6157 (ISSN-L)
1941-7330
DOI: 10.1214/08-aoas169
OpenAlex: W2052825782
Cited by: 2,714
References: 27
Citations by year: 2026: 357 · 2025: 341 · 2024: 354 · 2023: 324 · 2022: 296 · 2021: 278 · 2020: 183 · 2019: 148 · 2018: 95 · 2017: 80 · 2016: 75 · 2015: 51 · 2014: 44 · 2013: 25 · 2012: 17
FWCI: 14.21
Type: journal article
Language: en
Open access: green
arxiv.org
Locations: The Annals of Applied Statistics (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Project Euclid (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Institutes of Health
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: random survival forest, right-censored survival data, survival trees, missing data imputation, prognostic model, body mass, coronary artery disease

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