Ordinal regression methodology for ROC curves derived from correlated data.
We present an approach for the analysis of correlated ROC data, using ordinal regression models in conjunction with generalized estimating equations. The approach applies to the analysis of degree-of-suspicion data derived from multiple interpretations of the same diagnostic study and from the examination of the same patients with multiple diagnostic modalities. The regression models make it possible to incorporate patient and reader characteristics into the analysis, without having to resort to stratification. We illustrate the potential of the approach with analysis of data from two studies in diagnostic oncology.
Toledano
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Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
Gatsonis
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D003933:Diagnosis; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000000981:diagnostic imaging; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000473:pathology; D008279:Magnetic Resonance Imaging; D009367:Neoplasm Staging; D015588:Observer Variation; D012372:ROC Curve; D011860:Radiography, Abdominal; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D014057:Tomography, X-Ray Computed