Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices

Stat Med. 1997 Jul 15;16(13):1529-42. doi: 10.1002/(sici)1097-0258(19970715)16:13<1529::aid-sim565>;2-h.


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.

MeSH terms

  • Diagnostic Tests, Routine / statistics & numerical data*
  • Humans
  • Monte Carlo Method
  • Predictive Value of Tests*
  • ROC Curve*
  • Reference Values
  • Sensitivity and Specificity