Random survival forests

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.

• Publication year

  2008

• Venue

  The Annals of Applied Statistics

• Publication date

  2008-09-01

• Fields of study

  Medicine, Mathematics, Computer Science

• Identifiers
  DOI 10.1214/08-AOAS169arXiv 0811.1645
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Experimental Results

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