Random Survival Forests

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 forest...

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Bibliographic Details
Published in:The annals of applied statistics 2008-09, Vol.2 (3), p.841-860
Main Authors: Ishwaran, Hemant, Kogalur, Udaya B., Blackstone, Eugene H., Lauer, Michael S.
Format: Article
Language:English
Subjects:
Body mass index
Combinatorics
Combinatorics. Ordered structures
Conservation of events
Coronary artery bypass
cumulative hazard function
Data imputation
Datasets
ensemble
Error rates
Exact sciences and technology
Graph theory
Mathematics
Missing data
Mortality
Nonparametric inference
Obesity
out-of-bag
prediction error
Probability and statistics
Sciences and techniques of general use
Statistics
survival tree
Tree growth
Trees
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Summary: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.
ISSN:1932-6157
1941-7330
DOI:10.1214/08-AOAS169