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. 2017;36(2):85-96.
doi: 10.22283/qbs.2017.36.2.85.

A Selective Review on Random Survival Forests for High Dimensional Data

Hong Wang  1 , Gang Li  2
Affiliations

A Selective Review on Random Survival Forests for High Dimensional Data

Hong Wang et al. Quant Biosci. 2017.

Abstract

Over the past decades, there has been considerable interest in applying statistical machine learning methods in survival analysis. Ensemble based approaches, especially random survival forests, have been developed in a variety of contexts due to their high precision and non-parametric nature. This article aims to provide a timely review on recent developments and applications of random survival forests for time-to-event data with high dimensional covariates. This selective review begins with an introduction to the random survival forest framework, followed by a survey of recent developments on splitting criteria, variable selection, and other advanced topics of random survival forests for time-to-event data in high dimensional settings. We also discuss potential research directions for future research.

Keywords: Censoring; Random survival forest; Survival ensemble; Survival tree; Time-to-event data.

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Figures

Fig. 1.
Fig. 1.
Plot of the estimated log cumulative hazard functions for different Karnofsky scores.
Fig. 2.
Fig. 2.
A CART survival tree.
Fig. 3.
Fig. 3.
Variable importance scores by CART.
Fig. 4.
Fig. 4.
Variable importance scores by random survival forest.
Fig. 5.
Fig. 5.
Performance comparison between Cox, CART and RSF.
See this image and copyright information in PMC

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