Tree-structured prognostic classification for censored survival data: Validation of computationally inexpensive model selection criteria

Abdissa Negassa, Antonio Ciampi, Michal Abrahamowicz, Stanley Shapiro, Jean François Boivin

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

The performance of computationally inexpensive model selection criteria in the context of tree structured prediction is discussed. It is shown through a simulation study that no one model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is suggested and shown to perform satisfactorily. A computationally efficient method of tree-growing within the RECursive Partition and AMalgamation (RECPAM) framework is suggested. The computationally efficient algorithm gives identical results as the original RECPAM tree-growing algorithm. An example of medical data analysis for developing prognostic classification is presented.

Original languageEnglish (US)
Pages (from-to)289-317
Number of pages29
JournalJournal of Statistical Computation and Simulation
Volume67
Issue number4
DOIs
StatePublished - Jan 1 2000
Externally publishedYes

Keywords

  • Censored survival data
  • Prognostic classification
  • RECPAM
  • Regression trees
  • Two-stage approach

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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