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Review of Random Survival Forest Method Publisher



Rezaei M1 ; Tapak L2 ; Alimohammadian M3, 4, 5 ; Sadjadi A4, 5 ; Yaseri M1
Authors
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Authors Affiliations
  1. 1. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
  3. 3. Department of Human Ecology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Source: Journal of Biostatistics and Epidemiology Published:2020


Abstract

Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially medical sciences, due to their high accuracy and non-parametric nature and applicability in high-dimensional data sets. This paper aims to provide a methodological review and how to use random survival forests in the analysis of right-censored survival data. Method: We present a review article based on the latest research in the PubMed database on random survival forest model methodology. Results: This article begins with an introduction to tree-based methods, ensemble algorithms, and random forest (RF) method, followed by random survival forest framework, bootstrapped data and out-of-bag (OOB) ensemble estimators, review of performance evaluation indicators, how to select important variables, and other advanced topics of random survival forests for time-to-event data. Conclusion: When analyzing right-censored survival data with high-dimensional data, while the relationships between variables are complex and their interactions are taken into account, the nonparametric random survival forest (RSF) method determines important variables affecting survival times with high accuracy and speed and also does not need to test the restrictive assumptions. © 2020, Tehran University of Medical Sciences. All rights reserved.