A review of survival trees

Abstract

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.

Decision forest: Twenty years of research

“…2. A survival tree [18,19] is a decision tree that aims to predict the time until an event of interest occurs. The event might be a death of a patient or when a machine ceases to function.…”

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

“…Bou-Hamad et al assessed the RSF performance for predicting the survival of patients with primary biliary cirrhosis of the liver. The resulted IBS from a 10-fold cross validation certified the best implementation for RSF following with bagging (Bou-Hamad, Larocque, & Ben-Ameur, 2011). In another real data application to model the survival time of Iranian females with breast cancer, random forest showed the highest level of accuracy among other learning techniques (Montazeri, Montazeri, Montazeri, & Beigzadeh, 2015).…”

Survival Ensemble with Sparse Random Projections for Censored Clinical and Gene Expression Data

“…We choose decision trees as the base learner for our survival ensemble because it has the flexibility to accommodate high dimensional covariates and it is the most widely used nonparametric survival model [21]. The reasons why random projection is introduced are two-fold.…”