Tree‐based methods have become popular for analyzing complex data structures where the
primary goal is risk stratification of patients. Ensemble techniques improve the accuracy in …

Prediction models are postulated as useful tools to support tasks such as clinical decision
making and benchmarking. In particular, classification tree models have enjoyed much …

Classification and regression tree analyses identify subsets of a sample that differ on an
outcome. Discrimination of subsets is performed using recursive binary splitting on a set of …

Tree-based models are increasingly popular due to their ability to identify complex
relationships that are beyond the scope of parametric models. Survival tree methods adapt …

Survival trees are a popular alternative to parametric survival modeling when there are
interactions between the predictor variables or when the aim is to stratify patients into …

Besides serving as prediction models, classification trees are useful for finding important
predictor variables and identifying interesting subgroups in the data. These functions can be …

In biomedical research, the logistic regression model is the most commonly used method for
predicting the probability of a binary outcome. While many clinical researchers have …

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 …

A new splitting criterion is explored for the tree-based method of analysis of censored data.
This criterion is an extension of those used in Classification and Regression Trees (CART). It …

Many clinical studies follow patients over time and record the time until the occurrence of an
event of interest (eg, recovery, death,…). When patients drop out of the study or when their …