Predictive accuracy of covariates for event times.

TitlePredictive accuracy of covariates for event times.
Publication TypeJournal Article
Year of Publication2012
AuthorsChen, Li, D Y. Lin, and Donglin Zeng
Date Published2012 Sep

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.

Alternate JournalBiometrika
Original PublicationPredictive accuracy of covariates for event times.
PubMed ID23843671
PubMed Central IDPMC3635702
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA082659 / CA / NCI NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States