SAMPLE SIZE DETERMINATION FOR DIAGNOSTIC ACCURACY STUDIES INVOLVING BINORMAL ROC CURVE INDICES
Identifieur interne : 00BC18 ( Main/Exploration ); précédent : 00BC17; suivant : 00BC19SAMPLE SIZE DETERMINATION FOR DIAGNOSTIC ACCURACY STUDIES INVOLVING BINORMAL ROC CURVE INDICES
Auteurs : Nancy A. Obuchowski [États-Unis] ; Donna Katzman Mcclish [États-Unis]Source :
- Statistics in Medicine [ 0277-6715 ] ; 1997-07-15.
English descriptors
- Teeft :
- Abnormal, Abnormal patients, Abnormality, Alternative hypothesis, Binormal, Binormal data, Cartilage abnormality, Conservative estimate, Covariance, Curve indices, Curve parameters, Data sets, Diagnostic accuracy, Diagnostic accuracy studies, Diagnostic accuracy studies table, Diagnostic tests, Direct estimation, Empirical variance, Estimator, Expert mammographer, Expr, Full area, John wiley sons, Labroc, Maximum likelihood estimates, Mcclish, Normal distribution, Null hypothesis, Obuchowski, Partial area, Partial area index, Partial areas, Rating categories, Rating data, Rocfit, Rocpwr, Sample size, Sample size determination, Square root, Standard error, Statist, Test results, Total sample size, Variance.
Abstract
Receiver operating characteristic (ROC) curves and their associated indices are valuable tools for the assessment of the accuracy of diagnostic tests. The area under the ROC curve is a popular summary measure of the accuracy of a test. The full area under the ROC curve, however, has been criticized because it gives equal weight to all false positive error rates. Alternative indices include the area under the ROC curve in a particular range of false positive rates (‘partial’ area) and the sensitivity of the test for a single fixed false positive rate (FPR). We present a unified approach for computing sample size for binormal ROC curves and their indices. Our method uses Taylor series expansions to derive approximate large‐sample estimates of the variance and covariance of binormal ROC curve parameters. Several examples from diagnostic radiology illustrate the proposed method. © 1997 John Wiley & Sons, Ltd.
Url:
DOI: 10.1002/(SICI)1097-0258(19970715)16:13<1529::AID-SIM565>3.0.CO;2-H
Affiliations:
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Obuchowski</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics and Epidemiology, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195‐5196</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Correspondence address: Department of Biostatistics and Epidemiology, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195‐5196</wicri:regionArea><placeName><region type="state">Ohio</region></placeName></affiliation></author><author><name sortKey="Mcclish, Donna Katzman" sort="Mcclish, Donna Katzman" uniqKey="Mcclish D" first="Donna Katzman" last="Mcclish">Donna Katzman Mcclish</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Virginia Commonwealth University, 1101 E. 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The area under the ROC curve is a popular summary measure of the accuracy of a test. The full area under the ROC curve, however, has been criticized because it gives equal weight to all false positive error rates. Alternative indices include the area under the ROC curve in a particular range of false positive rates (‘partial’ area) and the sensitivity of the test for a single fixed false positive rate (FPR). We present a unified approach for computing sample size for binormal ROC curves and their indices. Our method uses Taylor series expansions to derive approximate large‐sample estimates of the variance and covariance of binormal ROC curve parameters. 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