A review of survival trees

By Imad Bou-Hamad, Denis Larocque, Hatem Ben-Ameur. 2011

222 citations recorded by the source corpus.

This paper presents a non–technical account of the developments in tree–based methods for the analysis of survival data with censoring. This review describes the initial developments, which mainly extended the existing basic tree methodologies to censored data as well as to more recent work. We also cover more complex models, more specialized methods, and more specific problems such as multivariate data, the use of time–varying covariates, discrete–scale survival data, and ensemble methods applied to survival trees. A data example is used to illustrate some methods that are implemented in R.

Subfields: Information Systems

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In Information Systems

1. Chandan K. Reddy
2. Yan Li
3. Ping Wang

Citations in the corpus

• Machine Learning for Survival Analysis: A Survey (2017, methodology): “ture. Another important aspect of building a survival tree is the selection of the final tree. Procedures such as backward selection or forward selection can be followed for choosing the optimal tree [Bou-Hamad et al. 2011]. However, an ensemble of trees (described in Section 4.5) can avoid the probl”