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

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journal contribution
posted on 2021-10-21, 15:40 authored by Takeshi Emura, Wei-Chern Hsu, Wen-Chi Chou

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.

Funding

The research of Emura T is funded by the grant from the Ministry of Science and Technology of Taiwan (MOST 107-2118-M-008 -003-MY3).

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