ROC-guided survival trees and ensembles

Yifei Sun, Sy Han Chiou, Mei Cheng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.

Original languageEnglish (US)
Pages (from-to)1177-1189
Number of pages13
JournalBiometrics
Volume76
Issue number4
DOIs
StatePublished - Dec 2020

Keywords

  • ROC curve
  • concordance index
  • risk prediction
  • time-dependent covariate
  • tree-based method

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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