uni.survival.tree: A Survival Tree Based on Stabilized Score Tests for High-dimensional Covariates

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).

Getting started

Package details

AuthorTakeshi Emura and Wei-Chern Hsu
MaintainerTakeshi Emura <takeshiemura@gmail.com>
Package repositoryView on CRAN
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uni.survival.tree documentation built on March 22, 2021, 9:05 a.m.