uni.survival.tree: A Survival Tree Based on Stabilized Score Tests for
A classification (decision) tree is constructed from survival data with high-dimensional covariates.
The method is a robust version of the logrank tree, where the variance is stabilized.
The main function "uni.tree" returns a classification tree for a given survival dataset.
The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests.
The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user).
This tree construction algorithm is proposed by Emura et al. (2021, in review).
Please use the canonical form
to link to this page.