A two-stage ROC curve regression model when sampling a population of diagnosticians

This paper presents a statistical method to explore and assess variability among diagnosticians in their accuracy and the association between accuracy and characteristics of diagnosticians and patients. The method assumes random sampling from a population of diagnosticians in addition to, and independent of, the random sampling from a population of patients. It is assumed the diagnosticians provide ordinal diagnostic ratings to all patients. In "Stage I", the Binormal Model is used to summarize the data into diagnostician-specific accuracy parameters at each patient covariate level. In "Stage II", the reduced data is then regressed on characteristics of the diagnosticians. Statistical inference is driven by bootstrapping. An application of the method to a national study of mammogram interpretation variability is presented. Empirical and theoretical evaluations are presented which substantiate the method. It will be shown that the model belongs to the well-known class of General Linear Models. The primary strength of the method is that it facilitates familiar and graphical approaches to the analysis of complex diagnostic ratings data arising from the simultaneous sampling of the population of diagnosticians as well as of the population of patients.