prognosticROC-package: Prognostic ROC curves

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

Description

This package computes prognostic ROC curves.

Details

The separation between two survival curves represents the magnitude of the association between the intervention and the time-to-event. A statistical test can determine the statistical significance of the difference but does not quantify its magnitude. The purpose of the prognostic ROC curve is to represent this uncertainty: the AUC is the probability that the time-to-event is improved in one arm compared to the other. This package is designed for computing such prognostic ROC curve with confidence intervals obtained by bootstrap resampling (from individual data only).

Package:	prognosticROC
Type:	Package
Version:	0.7
Date:	2013-11-27
License:	GPL (>=2)
LazyLoad:	yes

AggregatedPROC	Compute prognostic ROC curve based on survival function already determined
	by the user (parametric model, Kaplan-Meier curve, weighted estimations,...)
IndividualPROC	Compute prognostic ROC curve based on individual data, survival function
	are determined by the Kaplan-Meier estimator, confidence intervals are obtained
	by bootstrap

Author(s)

Y. Foucher <[e-mail]>

and C. Combescure <[e-mail]>

References

Combescure C, Perneger TV, Weber DC, Daures JP and Foucher Y. Prognostic ROC curves: a method for representing the overall discriminative capacity of binary markers with right-censored time-to-event endpoints. Manuscript in press. Epidemiology. 2013.

See Also

URL: http://www.divat.fr

prognosticROC documentation built on May 2, 2019, 3:45 a.m.