The empirical ROC curve is a powerful statistical tool to evaluate the precision of tests in several fields of study. This is a twodimensional plot where the horizontal and vertical axis represent false positive and true positive fraction respectively, also referred to as 1specificity and sensitivity, where precision is evaluated through a summary index, the area under the curve (AUC). Several computer tools are used to perform this analysis one of which is the R environment, this is an open source and free to use environment that allows the creation of different packages designed to perform the same tasks in distinct ways often resulting in different customization and features often providing similar results. There is a need to explore these different packages to provide an experienced user with the simplest and most robust execution of a needed analysis. This work catalogued the different R packages capable of ROC analysis exploring their performance. A shiny web application is presented that serves as a repository allowing for efficient use of all of these packages.

The ability of a medical test to differentiate between diseased and non-diseased states is of vital importance and must be screened by statistical analysis for reliability and improvement. The receiver operating characteristic (ROC) curve remains a popular method of marker analysis, disease screening and diagnosis. Covariates in this field related to the subject’s characteristics are incorporated in the analysis to avoid bias. The covariate adjusted ROC (AROC) curve was proposed as a method of incorporation. The AROC R-package was recently released and brings various methods of estimation based on multiple authors work. The aim of this study was to explore the AROC package functionality and usability using real data noting its possible limitations. The main methods of the package were capable of incorporating different and multiple variables, both categorical and continuous, in the AROC curve estimation. When tested for the same data, AROC curves are generated with no statistical differences, regardless of method. The package offers a variety of methods to estimate the AROC curve complemented with predictive checks and pooled ROC estimation. The package offers a way to conduct a more thorough ROC and AROC analysis, making it available for any R user.

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