Piecewise exponential survival trees with time-dependent covariates

Xin Huang, Shande Chen, Seng Jaw Soong

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Survival trees methods are nonparametric alternatives to the semiparametric Cox regression in survival analysis. In this paper, a tree- based method for censored survival data with time-dependent covariates is proposed. The proposed method assumes a very general model for the hazard function and is fully nonparametric. The recursive partitioning algorithm uses the likelihood estimation procedure to grow trees under a piecewise exponential structure that handles time-dependent covariates in a parallel way to time-independent covariates. In general, the estimated hazard at a node gives the risk for a group of individuals during a specific time period. Both cross-validation and bootstrap resampling techniques are implemented in the tree selection procedure. The performance of the proposed survival trees method is shown to be good through simulation and application to real data.

Original languageEnglish
Pages (from-to)1420-1433
Number of pages14
JournalBiometrics
Volume54
Issue number4
DOIs
StatePublished - 1 Dec 1998

Keywords

  • Classification and regression trees
  • Survival analysis
  • Survival trees
  • Time-dependent covariates

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