A survival tree based on stabilized score tests for high-dimensional covariates

A survival tree can classify subjects into different survival prognostic groups. However, when data contains high-dimensional covariates, the two popular classification trees exhibit fatal drawbacks. The logrank tree is unstable and tends to have false nodes; the conditional inference tree is diffic...

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Bibliographic Details
Published in:Journal of applied statistics Vol. 50; no. 2; pp. 264 - 290
Main Authors: Emura, Takeshi, Hsu, Wei-Chern, Chou, Wen-Chi
Format: Journal Article
Language:English
Published: England Taylor & Francis 25.01.2023
Taylor & Francis Ltd
Subjects:
Algorithms
CART
censoring
classification tree
Data analysis
gene expression
logrank test
Lung cancer
Nodes
prognostic prediction
Statistical methods
Survival
prognostic prediction
classification tree
censoring
logrank test
gene expression
CART
ISSN:0266-4763, 1360-0532
Online Access:Get full text
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Summary:A survival tree can classify subjects into different survival prognostic groups. However, when data contains high-dimensional covariates, the two popular classification trees exhibit fatal drawbacks. The logrank tree is unstable and tends to have false nodes; the conditional inference tree is difficult to interpret the adjusted P-value for high-dimensional tests. Motivated by these problems, we propose a new survival tree based on the stabilized score tests. We propose a novel matrix-based algorithm in order to tests a number of nodes simultaneously via stabilized score tests. We propose a recursive partitioning algorithm to construct a survival tree and develop our original R package uni.survival.tree ( https://cran.r-project.org/package=uni.survival.tree ) for implementation. Simulations are performed to demonstrate the superiority of the proposed method over the existing methods. The lung cancer data analysis demonstrates the usefulness of the proposed method.
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ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2021.1990224