# Difference between the areas under two curves

The difference between areas under the ROC curves compares the accuracy of two or more diagnostic tests.

It is imperative when comparing tests that you choose the correct type of analysis dependent on how you collect the data. If the tests are performed on the same subjects (paired design), the test results are usually correlated. Less commonly, you may perform the different tests on different groups of subjects or the same test on different groups of subjects, and the test results are independent. A paired design is more efficient and is preferred whenever possible.

A point estimate of the difference between the area under two curves is a single value that is the best estimate of the true unknown parameter; a confidence interval indicates the uncertainty of the estimate. If the tests are independent, the confidence interval uses the combined variance of the curves and a large sample Wald approximation. If the tests are paired, the standard error incorporates the covariance (DeLong et al., 1998) and uses a large sample Wald approximation.

- Equality
The null hypothesis states that the difference is equal to zero, against the alternative hypothesis that it is not equal to zero. When the test p-value is small, you can reject the null hypothesis and conclude the difference is not equal to zero, the tests are different.

- Equivalence
The null hypothesis states that the difference is less than a lower bound of practical equivalence or greater than an upper bound of practical equivalence, against the alternative hypothesis that the difference is within an interval considered practically equivalent. When the test p-value is small, you can reject the null hypothesis and conclude that the tests are equivalent.

- Non-inferiority
The null hypothesis states that the difference from a standard test is greater than the smallest practical difference against the alternative hypothesis that the difference from the standard test is less than the smallest practical difference. When the test p-value is small, you can reject the null hypothesis and conclude that the test is not inferior to the standard test.

When the ROC curves cross, the difference between the AUC does not provide much useful information.

**Related information**

- What is Analyse-it?
- What's new?
- Administrator's Guide
- User's Guide
- Statistical Reference Guide
- Distribution
- Compare groups
- Compare pairs
- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
- Factor analysis (FA)
- Item reliability
- Fit model
- Method comparison
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
- Measures of diagnostic accuracy
- Estimating sensitivity and specificity of a diagnostic test
- Comparing the sensitivity and specificity two diagnostic tests
- ROC plot
- Plotting a single ROC curve
- Comparing two or more ROC curves
- Area under the curve (AUC)
- Testing the area under the curve
- Difference between the areas under two curves
- Testing the difference between the areas under two curves
- Decision thresholds
- Decision plot
- Finding the optimal decision threshold
- Predicting the decision threshold
- Study design
- Control charts
- Process capability
- Pareto analysis
- Study Designs
- Bibliography

Version 6.00

Published 27-Apr-2022