Prognostic ROC curves for evaluating the predictive capacity of a binary test

Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.


Reference manual

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0.7 by Y. Foucher, 8 years ago,

Browse source code at

Authors: Y. Foucher <[email protected]> and C. Combescure <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Depends on splines, survival

See at CRAN