Random survival forests

TLDR

The authors introduce random survival forests, a random forests method for analyzing right‑censored survival data, and propose a conservation‑of‑events principle that defines ensemble mortality as a simple, interpretable predicted outcome. They develop new survival‑splitting rules, a missing‑data imputation algorithm, and illustrate the method with examples—including a coronary artery disease case study—implemented in the freely available R package randomSurvivalForest.

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.