descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 Sep 2008Embargo end date: 01 Jan 2008Publisher:Institute of Mathematical StatisticsJournal:The Annals of Applied Statistics, volume 2 (issn: 1932-6157,

Authors: Ishwaran, Hemant; Kogalur, Udaya B.; Blackstone, Eugene H.; Lauer, Michael S.;

doi: 10.1214/08-aoas169 , 10.48550/arxiv.0811.1645

arXiv: http://arxiv.org/abs/0811.1645

Random survival forests

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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.

Published in at http://dx.doi.org/10.1214/08-AOAS169 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Keywords

Conservation of events, FOS: Computer and information sciences, out-of-bag, prediction error, ensemble, Applications (stat.AP), cumulative hazard function, survival tree, Statistics - Applications

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