Basic ROC Analysis and Evaluation of Risk Prediction Markers

The pcvsuite package for R can be downloaded here: 

The package contains four main functions: roccurve, comproc, rocreg and predcurve. Briefly, the roccurve command plots an estimate of the ROC curve for one or more diagnostic tests (or biomarkers). Confidence intervals can be displayed for the TPF (true positive fraction) corresponding to a specified FPF (false positive fraction). Confidence intervals are calculated using the bootstrap. The comproc command calculates summary ROC indices for two tests along with confidence intervals for each and for the difference. A p-value for testing equality of the ROCs based on the summary indices is output. The rocreg command fits an ROC-GLM regression model. Covariate adjustment is accommodated in all three commands.

The predcurve uses the risk distribution associated with a marker or model to evaluate marker utility when applied to the population. The classification performance is optionally included in an integrated display of predictiveness and classification measures. Alternate graphical outputs include CDFs and densities of the risk estimation. Support for nested models, and for testing differences between two models is provided.Documentation on all three commands is also contained here:

Additionally, these articles (Stata J 2009;1:1-16Stata J 2009;1:17-19) published in the Stata Journal explain both commands and their options in more detail. Note that the syntax in the articles is all Stata-specific; however, the methods and rationale used to implement the functions in R remain the same. The arguments are also the same between R and Stata.

Predictiveness curves are described in this article: AmJEpid 2008;3:362-368.