Implementation of cumulative ROC curve analysis and extension to more than three outcome levels are straightforward. The procedure was also applied to publicly available data related to computer imaging and biomarker exposure science, yielding good to excellent AUCs, as well as cutpoints with sensitivities and specificities of commensurate quality. Cutpoints computed from maximum likelihood regression parameters demonstrated bias that was often negligible. Total Accuracy demonstrated the least absolute percent-bias. Simulations also compared several criteria for selecting cutpoints to discriminate outcome levels: the Youden Index, Matthews Correlation Coefficient, Total Accuracy, and Markedness. The procedure combines cumulative logit regression with a cumulative extension to the ROC curve and performs as expected with ternary (three-level) ordinal outcomes under a variety of simulated conditions (unbalanced data, proportional and non-proportional odds, areas under the ROC curve from 0.70 to 0.95). Cumulative receiver operator characteristic (ROC) curve analysis extends classic ROC curve analysis to discriminate three or more ordinal outcome levels on a shared continuous scale.
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