Predictive accuracy of covariates for event times

Li Chen, D. Y. Lin, Donglin Zeng

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


We propose a graphical measure, the generalized negative predictive function, to quantify the predictive accuracy of covariates for survival time or recurrent event times. This new measure characterizes the event-free probabilities over time conditional on a thresholded linear combination of covariates and has direct clinical utility. We show that this function is maximized at the set of covariates truly related to event times and thus can be used to compare the predictive accuracy of different sets of covariates. We construct nonparametric estimators for this function under right censoring and prove that the proposed estimators, upon proper normalization, converge weakly to zero-mean Gaussian processes. To bypass the estimation of complex density functions involved in the asymptotic variances, we adopt the bootstrap approach and establish its validity. Simulation studies demonstrate that the proposed methods perform well in practical situations. Two clinical studies are presented.

Original languageEnglish
Pages (from-to)615-630
Number of pages16
Issue number3
StatePublished - Sep 2012

Bibliographical note

Funding Information:
This research was supported by the National Institutes of Health, U.S.A. The authors thank two referees and an associate editor for their helpful comments.


  • Censoring
  • Negative predictive value
  • Positive predictive value
  • Prognostic accuracy
  • Receiver operating characteristic curve
  • Recurrent event
  • Survival data
  • Transformation model

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics (all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences (all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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