Survival trees: a pathway among features and open issues of the main R packages


Survival analysis aims to study the occurrence of a particular event during a follow-up period. Recently, many machine learning methods have been used for analyzing right-censored data. Among these, survival trees are a useful tool of recursive partitioning for defining homogeneous groups in terms of survival probability. However, there are still some unclear points on how to work with these methods from a theoretical and practical point of view. Indeed, even if there are a lot of proposed methods, many of these present little documentation and there does not exist an harmonization of all these proposals. This work aims to shed light on the topic and to provide a practical guide for simulating survival data, fitting survival trees and evaluating their performance with the statistical software R.

DOI Code: 10.1285/i20705948v15n3p479

Keywords: Survival data; Recursive partitioning; Machine learning; Simulations

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