[1] ROCCET is designed specifically for performing and assessing a standard binary classification test (disease vs. control).
It operates through a menu-based navigation system that allows users to identify or assess those clinical variables and/or metabolites that contain the maximal diagnostic or class-predictive information.
In medical biomarker studies it is becoming increasingly common to report this tradeoff in sensitivity and specificity using a Receiver Operating Characteristic (ROC) curve.
In the univariate module single variables are evaluated (by a t-test) and ranked for their separation performance (i.e. the AUC of the ROC), including confidence intervals (CI) and a computed optimal threshold.
In the multivariate module one can choose between three different techniques – SVM (support vector machine), PLS-DA (partial least squares discriminant analysis) and Random Forests for classifying and selecting metabolites or clinical variables for an optimal ROC performance.
The resulting analysis produces the top-performing multi-variable model(s) based on their ROC curve characteristics.