A Bivariate Binormal Model for Modelling Double Reading of Screening Mammograms

Double reading of screening mammograms, a feature of many breast cancer screening programs, is impacted by interactions between the two image readers. In this work, we describe how the bivariate binormal (BVBN) model, originally developed for statistical analysis of reader studies, can be used to analyze double reading of screening mammograms. The model posits two bivariate normal distributions that describe the distribution of latent decision variables of the two readers for cancer and non-cancer cases. The BVBN allows for the estimation of correlation coefficients between the decision variables of two readers, independent of performance and the threshold for recall. We contend that these correlation coefficients are a useful way to characterize interactions between readers because they characterize associations at the level of the perceptual response in a way that is consistent with Signal Detection Theory. We describe the BVBN model and show how parameters can be estimated from count data under an assumed multinomial distribution. The analysis presented focuses on two aspects of the BVBN model. For implementation using binary data, an equal-variance assumption on latent decision variables is required. Otherwise, the model is over-parameterized. We characterize and discuss the consequence of this assumption. We also show how disagreement rates, an alternative measure of reader interactions, suffer from base-rate effects making them more difficult to interpret than the correlation coefficients of the BVBN model.