Estimation of predictive accuracy in survival analysis using R and S-PLUS

Research output: Contribution to journalArticlepeer-review


When the purpose of a survival regression model is to predict future outcomes, the predictive accuracy of the model needs to be evaluated before practical application. Various measures of predictive accuracy have been proposed for survival data, none of which has been adopted as a standard, and their inclusion in statistical software is disregarded. We developed the surev library for R and S-PLUS, which includes functions for evaluating the predictive accuracy measures proposed by Schemper and Henderson. The library evaluates the predictive accuracy of parametric regression models and of Cox models. The predictive accuracy of the Cox model can be obtained also when time-dependent covariates are included because of non-proportional hazards or when using Bayesian model averaging. The use of the library is illustrated with examples based on a real data set.

Original languageEnglish
Pages (from-to)132-137
Number of pages6
JournalComputer Methods and Programs in Biomedicine
Issue number2
Publication statusPublished - Aug 2007


  • Bayesian model averaging
  • Cox model
  • Parametric models
  • Predictive accuracy
  • R
  • S-PLUS
  • Survival analysis

ASJC Scopus subject areas

  • Software


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