Premium
SAMPLE SIZE DETERMINATION FOR DIAGNOSTIC ACCURACY STUDIES INVOLVING BINORMAL ROC CURVE INDICES
Author(s) -
OBUCHOWSKI NANCY A.,
McCLISH DONNA KATZMAN
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970715)16:13<1529::aid-sim565>3.0.co;2-h
Subject(s) - receiver operating characteristic , statistics , mathematics , range (aeronautics) , covariance , sensitivity (control systems) , sample size determination , variance (accounting) , false positive rate , sample (material) , diagnostic accuracy , medicine , radiology , materials science , chemistry , accounting , chromatography , electronic engineering , engineering , business , composite material
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.