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Partial AUC estimation and regression.

Authors
  • Dodd, Lori E1
  • Pepe, Margaret S
  • 1 Biometric Research Branch, National Cancer Institute, 6130 Executive Blvd, MSC 7434, Rockville, Maryland 20892, USA. [email protected]
Type
Published Article
Journal
Biometrics
Publication Date
September 2003
Volume
59
Issue
3
Pages
614–623
Identifiers
PMID: 14601762
Source
Medline
License
Unknown

Abstract

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.

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