201. Random survival forests
- Author
-
Michael S. Lauer, Eugene H. Blackstone, Hemant Ishwaran, and Udaya B. Kogalur
- Subjects
Statistics and Probability ,FOS: Computer and information sciences ,0303 health sciences ,out-of-bag ,prediction error ,Computer science ,Random survival forests ,ensemble ,Missing data ,survival tree ,Measure (mathematics) ,Statistics - Applications ,Outcome (probability) ,Random forest ,Conservation of events ,03 medical and health sciences ,0302 clinical medicine ,Survival data ,030220 oncology & carcinogenesis ,Modeling and Simulation ,Statistics ,Applications (stat.AP) ,Statistics, Probability and Uncertainty ,cumulative hazard function ,030304 developmental biology - 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)
- Published
- 2008