Nonparametric analysis of clustered ROC curve data

Biometrics. 1997 Jun;53(2):567-78.


Current methods for estimating the accuracy of diagnostic tests require independence of the test results in the sample. However, cases in which there are multiple test results from the same patient are quite common. In such cases, estimation and inference of the accuracy of diagnostic tests must account for intracluster correlation. In the present paper, the structural components method of DeLong, DeLong, and Clarke-Pearson (1988, Biometrics 44, 837-844) is extended to the estimation of the Receiver Operating Characteristics (ROC) curve area for clustered data, incorporating the concepts of design effect and effective sample size used by Rao and Scott (1992, Biometrics 48, 577-585) for clustered binary data. Results of a Monte Carlo simulation study indicate that the size of statistical tests that assume independence is inflated in the presence of intracluster correlation. The proposed method, on the other hand, appropriately handles a wide variety of intracluster correlations, e.g., correlations between true disease statuses and between test results. In addition, the method can be applied to both continuous and ordinal test results. A strategy for estimating sample size requirements for future studies using clustered data is discussed.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Angiography, Digital Subtraction
  • Biometry
  • Carotid Stenosis / diagnosis
  • Carotid Stenosis / diagnostic imaging
  • Cluster Analysis*
  • Computer Simulation
  • Data Interpretation, Statistical*
  • Diagnosis
  • Humans
  • Magnetic Resonance Angiography
  • Monte Carlo Method
  • ROC Curve*
  • Sensitivity and Specificity