Random survival forests
Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone, Michael S., Lauer

TL;DR
This paper presents random survival forests, a novel method for analyzing right-censored survival data, introducing new splitting rules, missing data algorithms, and an interpretable mortality measure, with implementation in R.
Contribution
It introduces random survival forests with new survival splitting rules, a missing data algorithm, and an ensemble mortality measure, advancing survival analysis techniques.
Findings
Effective in analyzing right-censored data
Provides interpretable mortality predictions
Implemented in accessible R package
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.
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