Random survival forests
Open accessAbstract
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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Random forests have been extended to the survival context by Ishwaran et al. (2008), who prove consistency of the Random Survival Forests (RSF) algorithm assuming that all variables are categorical.
“Random forests have been extended to the survival context by Ishwaran et al. (2008), who prove consistency of Random Survival Forests (RSF) algorithm assuming that all variables are categorical.”
From A random forest guided tour (2016) · cites this paper as Ishwaran et al. (2008)
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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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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