Discordance Analysis Characteristics as a New Method to Compare the Diagnostic Accuracy of Tests: Example of Complexed Versus Total Prostate-Specific Antigen

Abstract

Abstract Background: ROC curve analysis is used to compare the overall diagnostic accuracy of tests, but its application to subgroups selected by a concentration range of only one marker may show severe biases. We developed a new approach, which we have named discordance analysis characteristics (DAC). Methods: The DAC method is based on a generalization of the McNemar test so that for a given pair of cutoff values only those patients are analyzed who are categorized differently by the two tests compared. The analyses are performed for all cutoff pairs that deliver identical sensitivities for both tests. We used data for total (tPSA) and complexed prostate-specific antigen (cPSA) from a recently published multicenter study to demonstrate the DAC method. Results: The example shows that ROC analyses of subgroups can give contradictory results about the diagnostic accuracy of two markers, depending on the marker used for the selection of subgroups. The DAC method avoids artifacts attributable to questionable selection of subgroups and facilitates overall and local comparisons of the diagnostic accuracy of tests. The DAC results of the analyzed data set suggest that cPSA has higher diagnostic accuracy than does tPSA. Conclusions: The DAC method is a suitable tool for comparing the clinical usefulness of laboratory markers. The DAC method could be considered as an additional tool to ROC analysis and could replace comparative ROC analyses of diagnostic tests, especially within subgroups defined by only one of the markers.