Tree-structured prediction for censored survival data and the cox model

Antonio Ciampi, Abdissa Negassa, Zihyi Lou

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


Prediction trees for the analysis of survival data are discussed. It is shown that trees are useful not only in summarizing the prognostic information contained in a set of covariates (prognostic classification), but also in detecting and displaying treatment-covariates interactions (subgroup analysis). The RECPAM approach to tree-growing is outlined; prognostic classification and subgroup analysis are then formulated within the RECPAM framework and on the basis of the Cox proportional hazards models with a priori strata. Two examples of data analysis are presented. The issue of cross-validation is discussed in relation to computationally cheaper model selection criteria.

Original languageEnglish (US)
Pages (from-to)675-689
Number of pages15
JournalJournal of Clinical Epidemiology
Issue number5
StatePublished - May 1995
Externally publishedYes


  • Censored survival data
  • Prognostic classification
  • Regression trees
  • Subgroup analysis

ASJC Scopus subject areas

  • Epidemiology


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