Semi-Parametric Inference for the Partial Area Under the ROC Curve
Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful one-number summary index...
| Main Author: | |
|---|---|
| Format: | Others |
| Published: |
Digital Archive @ GSU
2008
|
| Subjects: | |
| Online Access: | http://digitalarchive.gsu.edu/math_theses/61 http://digitalarchive.gsu.edu/cgi/viewcontent.cgi?article=1060&context=math_theses |
| Summary: | Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful one-number summary index for the diagnostic accuracy of the test. When only a particular region of the ROC curve would be of interest, the partial AUC (pAUC) is a more appropriate index for the diagnostic accuracy. In this thesis, we develop seven confidence intervals for the pAUC under the semi-parametric models for the diseased and non-diseased populations by using the normal approximation, bootstrap and empirical likelihood methods. In addition, we conduct simulation studies to compare the finite sample performance of the proposed confidence intervals for the pAUC. A real example is also used to illustrate the application of the recommended intervals. |
|---|