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Comparison of partial areas under the ROC curve

Next selectROC curves
Next selectComparison of partial areas under ROC curve


Partial area under the ROC curve for a pre-specified specififity interval

When comparing 2 ROC curves, it may occur that the two Areas under the ROC curve (AUC) are equal, but one has a higher sensitivity than the other in a specificity range. The example shows two crossing ROC curves with equal total AUC. However, for the clinically important range (specificity higher than 80%), the sensitivity of test A is clearly higher than of test B (adapted from Obuchowski, 2006).

MedCalc allows to compare the two partial areas below the ROC curve in that specific interval. The data for the two partial areas can be derived from the same subjects (samples, patients, ...), in which case you have paired data; or from different subjects, in which case you have independent data and two independent partial areas.

See Partial area under ROC curve for detailed information on the calculation and interpretation of partial areas under the ROC curve.

Required input

Dialog box for comparison of Partial areas under ROC curve

Results - Paired samples

The following report is displayed in case of a paired design:

Comparison of Partial areas under ROC curve statistics (specificity interval)

First MedCalc shows the parameters of the analysis.

Next, the following are reported for the two variables:

Results - Independent samples

The report for independent samples is somewhat different, but essentially contains the same statistics:

Comparison of Partial areas under ROC curve statistics (specificity interval)

Comparison of the two partial areas

MedCalc reports the difference between the two partial areas with the 95% bootstrap confidence interval for the difference. If the confidence interval does not include 0, then it can be concluded that the two partial areas are significantly different (P<0.05).


The ROC curve and partial areas are displayed in a separate window:

Comparison of Partial areas under ROC curve graph (specificity interval)


See also

External links

Recommended book

Book cover

Statistical Methods in Diagnostic Medicine
Xiao-Hua Zhou, Nancy A. Obuchowski, Donna K. McClish

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Statistical Methods in Diagnostic Medicine provides a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy.