A bivariate binormal ROC methodology for comparing new methods to an existing standard for screening applications

Validating the use of new imaging technologies for screening large patient populations is an important and very challenging area of diagnostic imaging research. A particular concern in ROC studies evaluating screening technologies is the problem of verification bias, in which an independent verification of disease status is only available for a subpopulation of patients, typically those with positive results by a current screening standard. For example, in screening mammography, a study might evaluate a new approach using a sample of patients that have undergone needle biopsy following a standard mammogram and subsequent work-up. This case sampling approach provides accurate independent verification of ground truth and increases the prevalence of disease cases. However, the selection criteria will likely bias results of the study. In this work we present an initial exploration of an approach to correcting this bias within the parametric framework of binormal assumptions. We posit conditionally bivariate normal distributions on the latent decision variable for both the new methodology as well as the screening standard. In this case, verification bias can be seen as the effect of missing data from an operating point in the screening standard. We examine the magnitude of this bias in the setting of breast cancer screening with mammography, and we derive a maximum likelihood approach to estimating bias corrected ROC curves in this model.