Partial AUC Estimation and Regression
Version of Record online: 26 AUG 2003
Volume 59, Issue 3, pages 614–623, September 2003
How to Cite
Dodd, L. E. and Pepe, M. S. (2003), Partial AUC Estimation and Regression. Biometrics, 59: 614–623. doi: 10.1111/1541-0420.00071
- Issue online: 26 AUG 2003
- Version of Record online: 26 AUG 2003
- Received July 2002. Revised March 2003. Accepted March 2003.
- Diagnostic testing;
- Mann-Whitney U-statistic;
- Receiver operating characteristic curve
Summary. Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer.