  The receiver operating characteristic (ROC) curve is the most popular tool
used to evaluate the discriminatory capability of diagnostic tests/biomarkers
measured on a continuous scale when distinguishing between two alternative
disease states (e.g, diseased and nondiseased). In some circumstances, the
test's performance and its discriminatory ability may vary according to
subject-specific characteristics or different test settings. In such cases,
information-specific accuracy measures, such as the covariate-specific and the
covariate-adjusted ROC curve are needed, as ignoring covariate information may
lead to biased or erroneous results. This paper introduces the R package
ROCnReg that allows estimating the pooled (unadjusted) ROC curve, the
covariate-specific ROC curve, and the covariate-adjusted ROC curve by different
methods, both from (semi) parametric and nonparametric perspectives and within
Bayesian and frequentist paradigms. From the estimated ROC curve (pooled,
covariate-specific or covariate-adjusted), several summary measures of
accuracy, such as the (partial) area under the ROC curve and the Youden index,
can be obtained. The package also provides functions to obtain ROC-based
optimal threshold values using several criteria, namely, the Youden Index
criterion and the criterion that sets a target value for the false positive
fraction. For the Bayesian methods, we provide tools for assessing model fit
via posterior predictive checks, while model choice can be carried out via
several information criteria. Numerical and graphical outputs are provided for
all methods. The package is illustrated through the analyses of data from an
endocrine study where the aim is to assess the capability of the body mass
index to detect the presence or absence of cardiovascular disease risk factors.
The package is available from CRAN at
https://CRAN.R-project.org/package=ROCnReg.
