The most frequently used model for simulating multireader multicase (MRMC) data that emulate confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz model, proposed by Roe and Metz in 1997 and later generalized by Hillis (2012), Abbey et al (2013) and Gallas and Hillis (2014). All of these models generate continuous confidence-of-disease ratings based on an underlying binormal model for each reader, with the separation between the normal and abnormal rating distributions varying across readers.

  Numerous studies have used these models for evaluating MRMC analysis and sample size methods. The models suggested in these papers for assessing type I error have been "null" models, where the expected AUC across readers is the same for each test. However, for the null models that have been suggested, there are other differences that would not exist if the two tests were identical.