Partial AUC Estimation and Regression

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 ac...

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Bibliographic Details
Published in:Biometrics Vol. 59; no. 3; pp. 614 - 623
Main Authors: Dodd, Lori E., Pepe, Margaret S.
Format: Journal Article
Language:English
Published: 350 Main Street , Malden , MA 02148 , U.S.A , and P.O. Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K Blackwell Publishing 01.09.2003
International Biometric Society
Subjects:
Area Under Curve
Biometrics
Biometry
Diagnostic testing
Estimation bias
Estimation methods
Estimators
Humans
Male
Mann-Whitney U-statistic
Maximum likelihood estimation
Modeling
Models, Statistical
Parametric models
Prostate cancer
Prostate-Specific Antigen - blood
Prostatic Neoplasms - blood
Prostatic Neoplasms - diagnosis
Receiver operating characteristic curve
Regression
Regression Analysis
Standard error
Statistics, Nonparametric
ISSN:0006-341X, 1541-0420
Online Access:Get full text
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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.
Bibliography:istex:406D685DB7B1705DC79CB0A5B8EAC7A3DADEAAB2
ark:/67375/WNG-7685KQD3-S
ArticleID:BIOM071
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0006-341X
1541-0420
DOI:10.1111/1541-0420.00071