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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Vydáno v:	Biometrics Ročník 59; číslo 3; s. 614 - 623
Hlavní autoři:	Dodd, Lori E., Pepe, Margaret S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	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
Témata:	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
On-line přístup:	Získat plný text
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Shrnutí:	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.
Bibliografie:	istex:406D685DB7B1705DC79CB0A5B8EAC7A3DADEAAB2 ark:/67375/WNG-7685KQD3-S ArticleID:BIOM071 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0006-341X 1541-0420
DOI:	10.1111/1541-0420.00071