Three methods for analysing correlated ROC curves: a comparison in real data sets from multi-reader, multi-case studies with a factorial design.
This paper compares three published methods for analysing multiple correlated ROC curves: a method using generalized estimating equations with marginal non-proportional ordinal regression models; a method using jackknifed pseudovalues of summary statistics; a method using a corrected F-test from analysis of variance of summary statistics. Use of these methods is illustrated through six real data examples from studies with the common factorial design, that is, multiple readers interpreting images obtained with each test modality on each study subject. The issue of the difference between typical summary statistics and summary statistics from typical ROC curves is explored. The examples also address similarities and differences among the analytical methods. In particular, while point estimates of differences between test modalities are similar, the standard errors of these differences do not agree for all three methods. A simulation study supports the standard errors provided by the generalized estimating equations with marginal non-proportional ordinal regression models.
Toledano
Alicia Y
AY
Center for Statistical Sciences, Department of Community Health, Brown University, Providence, Rhode Island 02912, USA. toledano@stat.brown.edu
D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D006801:Humans; D012372:ROC Curve; D012044:Regression Analysis