Annals of Applied Statistics

Random survival forests

Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone, and Michael S. Lauer

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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.

Article information

Source
Ann. Appl. Stat., Volume 2, Number 3 (2008), 841-860.

Dates
First available in Project Euclid: 13 October 2008

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1223908043

Digital Object Identifier
doi:10.1214/08-AOAS169

Mathematical Reviews number (MathSciNet)
MR2516796

Zentralblatt MATH identifier
1149.62331

Keywords
Conservation of events cumulative hazard function ensemble out-of-bag prediction error survival tree

Citation

Ishwaran, Hemant; Kogalur, Udaya B.; Blackstone, Eugene H.; Lauer, Michael S. Random survival forests. Ann. Appl. Stat. 2 (2008), no. 3, 841--860. doi:10.1214/08-AOAS169. https://projecteuclid.org/euclid.aoas/1223908043


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