ROC curve and sensitivity-specificity plots summarizing performance of AuDIT in identifying inaccurate and imprecise transitions, as evaluated by an expert. AuDIT uses the t-test p-value and the CV of the PAR (ratio of analyte peak area to SIS peak area) to detect problem transitions. (A), Both the p-value and the CV are required to achieve acceptable performance (i.e., as indicated by AUC values in parentheses). (B), Specificity and sensitivity values achieved as the p-value threshold is varied from 0 to 1 (with a fixed CV threshold of 20%). The chosen p-value threshold of 10-5 used for all of the analyzed data is indicated by the red circle (sensitivity, 98%; specificity, 97%). The rainbow color bar (right y axis) keys the location of the p-value threshold on the sensitivity-specificity curve. Adapted from Abbatiello, Mani, et. al., Clinical Chemistry, 56, 291-305 .
Mani et al. BMC Bioinformatics 2012 13(Suppl 16):S9 doi:10.1186/1471-2105-13-S16-S9