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We also examined the discrimination performance of the prognostic index between early and late relapse in patients by plotting a receiver operating characteristic (ROC) curve for each dataset (JMP).
23, 24 The performance of the computer color analysis was compared with the latent class (serving as surrogate gold standard) by plotting a receiver operating characteristics (ROC) curve and measuring the area under the curve (AUC).
The correlation between these variables and changes in CO after fluid expansion and their value for predicting an increase in CO after fluid expansion were calculated by plotting a receiver operating characteristic (ROC) curve.
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By varying the threshold values, we are able to plot a receiver operating characteristic curve, which shows the relationship between sensitivity and 1-specificity.
We assess globally how these statistics vary with K by plotting the receiver operating characteristic (ROC) curve (recall as a function of fall-out) and the precision-recall curve (precision as a function of recall), and computing the area under these curves (respectively AUROC and AUPR) normalized between 0 and 1.
Discrimination was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC) [14], [15].
The cut-off value was ≥ 0.848, calculated by plotting the Receiver Operating Characteristic (ROC) curve.
Furthermore, we assessed the classification performance by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC).
Discrimination was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC) or c statistic.
Heterogeneity due to threshold effect was explored by plotting summary receiver operating curves (sROC) for each diagnostic method to assess if the points in the plots had a curvilinear (shoulder arm) pattern or not.
The accuracy of each score in predicting outcome was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC) with 95% confidence intervals [ 16].
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