Wilcoxon-based group sequential designs for comparison of areas under two correlated ROC curves

Stat Med. 2008 Jan 30;27(2):213-23. doi: 10.1002/sim.2856.

Abstract

Clinical studies to evaluate the relative accuracies of two diagnostic modalities via their receiver operating characteristic (ROC) curves are currently conducted using fixed sample designs: cases are accrued until a predetermined sample size is achieved and, at that point, the areas under the ROC curves are computed and compared (Radiology 1982; 143:29-36; Radiology 1983; 148:839-843). In prospective ROC studies (Radiology 1990; 175:571-575), participants are recruited from a clinically defined cohort and diagnostic test information is obtained and interpreted in advance of ascertaining the definitive proof of diagnosis ('gold standard'). In retrospective studies, cases are selected from a set of patient records and their diagnostic tests are interpreted without knowledge of the 'gold standard'. The conduct of ROC studies requires considerable effort and resources, particularly for the collection of 'gold standard' information. Thus, it is highly desirable to search for designs which are more efficient than using a fixed sample. In this paper, we discuss the formulation and application of group sequential designs (GSDs) to comparative ROC studies based on non-parametric Wilcoxon estimators of the area under the ROC curves. The approach is applicable to comparisons of ROC curve areas of two tests measured on either continuous or ordinal scales on same cases ('paired' designs) with one reader. The adoption of GSDs may lead to substantial savings in the number of required cases, thus resulting in both time and resource use efficiency.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Clinical Trials as Topic / statistics & numerical data*
  • Data Interpretation, Statistical
  • Diagnostic Tests, Routine / standards
  • Diagnostic Tests, Routine / statistics & numerical data
  • Humans
  • Models, Statistical
  • ROC Curve*
  • Reproducibility of Results
  • Statistics, Nonparametric
  • United States