Conclusions
The cumulative ROC curve method comprises a straightforward combination of cumulative logit regression with ROC curve analysis, and is readily implemented with available statistical software. Cutpoint selection criteria from classic ROC curve analysis are still applicable, as well as established performance measures, such as sensitivity, specificity, and AUC. Cumulative ROC curve analysis performed as expected under simulation and with real-world data for a variety of conditions, including balanced and unbalanced data, proportional and non-proportional odds assumptions for the cumulative logit model, and AUCs associated with fair, good, and excellent performance (AUC = 0.70 -- 0.95). Of the ROC curve-based cutpoint criteria, Total Accuracy was the least biased in simulation compared to the Youden Index, Mathews Correlation Coefficient, and Markedness. Calculation of cutpoints from cumulative logit regression parameters, which forgoes evaluation of cumulative ROC curves, demonstrated minimal bias, owing to parameter estimation with maximum likelihood methods. Since parametric cutpoints are calculated from the ratio of MLE model parameters, variance was calculated with Fieller's Method. Cumulative ROC curve analysis and parametric cutpoints for ternary ordinal outcomes \(\left(J=3\right)\) were implemented with version 9.4 of the SAS statistical software, and the author's programs are freely available for download without warranty or guarantee from the author's GitHub site (bitly URL).