Interrelationships Among Sensitivity, Precision, Accuracy, Specificity and Predictive Values in Bioassays, Represented as Combined ROC Curves with Integrated Cutoff Distribution Curves and Novel Index Values.

This work introduces accuracy- and precision-ROC curves in addition to SS- and PV-ROC curves and shows a novel means of profiling biomarker characteristics for validation of optimal cutoffs in clinical diagnostics and decision making. This investigation included 91 patients with a confirmed bladder cancer diagnosis and 1152 patients without evidence of cancer. The study performed a quantitative investigation of used-up test cassettes from the visual UBC Rapid qualitative point-of-care assay, which had already been applied in routine diagnostics. Using a photometric reader, quantitative data could also be obtained from the test line of the used cassettes. The ROC curves were constructed using different thresholds or cutoff levels to determine the TP/TN and FP/FN values for each threshold at concentrations of 5, 10, 30, 50, 90, 110, 250 and 300 µg/L. The resulting TP/TN and FP/FN values were used to calculate the sensitivity/specificity, accuracy, precision and predictive values in order to plot the ROC curves with integrated cutoff value distributions and their index cutoff diagrams. A common, optimal cutoff value for all the diagnostic parameters was derived with the aid of an ROC index cutoff diagram. It includes higher specificity and an acceptable number of NPVs. As a result, use of a sensitivity-specificity ROC curve and the Youden index only permits the selection of a maximal threshold value or cutoff point for the biomarker of interest but disregards the clinical status of the patient, whereas the precision, accuracy and predictive values give information related to the disease. This work's novelty compared to the existing methodology includes the first international publication of accuracy- and precision-ROC curves. It enables the investigation of the relationship among the sensitivity, specificity, precision, accuracy and predictive values at varied cutoff levels within a bioassay, presenting these in a single graph consisting of selected receiver operating characteristic (ROC) curves for each parameter, including cutoff distribution curves. This is a transparent method to identify appropriate cutoffs for multiple diagnostic parameters. According to the results, the single-sided use of a sensitivity-specificity ROC curve including the maximal Youden index value as an optimal cutoff or single-point determinations for predictive values cannot provide diagnostic information of the same quality as that given by a multi-parameter diagnostic profile and a multi-parameter cutoff-index-diagram-derived optimal value as presented within this work. The proposed multi-parameter cutoff-index diagram includes novel index cutoff AOX. It is a new different method that allows a quantitative comparison of the results from multi-parameter ROC curves, which cannot be performed with the AUC. However, the methods are different and do not exclude each other.