A general regression methodology for ROC curve estimation.
A method for applying generalized ordinal regression models to categorical rating data to estimate and analyze receiver operating characteristic (ROC) curves is presented. These models permit parsimonious adjustment of ROC curve parameters for relevant covariates through two regression equations that correspond to location and scale. Particular shapes of ROC curves are interpreted in relation to the kind of covariates included in the two regressions. The model is shown to be flexible because it is not restricted to the assumption of binormality that is commonly employed in smoothed ROC curve estimation, although the binormal model is one particular form of the more general model. The new method provides a mechanism for pinpointing the effect that interobserver variability has on the ROC curve. It also allows for the adjustment of ROC curves for temporal variation and case mix, and provides a way to assess the incremental diagnostic value of a test. The new methodology is recommended because it substantially improves the ability to assess diagnostic tests using ROC curves.
Tosteson
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AN
Division of Biostatistics and Epidemiology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Begg
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CB
D001943:Breast Neoplasms; D003110:Colonic Neoplasms; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis / Q000556:secondary; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012372:ROC Curve; D012044:Regression Analysis; D014463:Ultrasonography