2008/09/01 by Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone +1 · 8 citations
Mathematics · Social Sciences · #Insurance, Mortality, Demography, Risk Management #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.AP
paper · pdf · doi:10.1214/08-aoas169
published as Annals of Applied Statistics 2008, Vol. 2, No. 3, 841-860 · 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)
openalex publication_date 2008/09/01 · arxiv created 2008/11/11 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/04
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.