articleThe Annals of Applied StatisticsSep 1, 2008HYBRID OA

Random survival forests

HIHemant IshwaranUBUdaya B. KogalurEHEugene H. BlackstoneMSMichael S. Lauer
Indexed inarxivcrossref

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.

Citation impact

2,413
total citations
FWCI
13.00
Percentile
100%
References
27
Citations per year

Authors

4
  • HI
    Hemant IshwaranCorresponding
  • UB
    Udaya B. Kogalur
  • EH
    Eugene H. Blackstone
  • MS
    Michael S. Lauer

Topics & keywords

Topics
Keywords
  • Random forest
  • Survival analysis
  • Missing data
  • Computation
  • Simple (philosophy)
  • Measure (data warehouse)
No related works found for this paper.

Funding