Data di Pubblicazione:
2022
Abstract:
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 practical point of view. Indeed, even if there
are a lot of proposed methods, many of these present little documentation,
mainly concerning the corresponding R functions. Moreover, 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.
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 practical point of view. Indeed, even if there
are a lot of proposed methods, many of these present little documentation,
mainly concerning the corresponding R functions. Moreover, 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.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Survival Data, Recursive Partitioning, Machine Learning, Simulations
Elenco autori:
Macis, Ambra
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