Improving the reliability of diagnostic tests in population-based agreement studies.

Many large-scale studies have recently been carried out to assess the reliability of diagnostic procedures, such as mammography for the detection of breast cancer. The large numbers of raters and subjects involved raise new challenges in how to measure agreement in these types of studies. An important motivator of these studies is the identification of factors that contribute to the often wide discrepancies observed between raters' classifications, such as a rater's experience, in order to improve the reliability of the diagnostic process of interest. Incorporating covariate information into the agreement model is a key component in addressing these questions. Few agreement models are currently available that jointly model larger numbers of raters and subjects and incorporate covariate information. In this paper, we extend a recently developed population-based model and measure of agreement for binary ratings to incorporate covariate information using the class of generalized linear mixed models with a probit link function. Important information on factors related to the subjects and raters can be included as fixed and/or random effects in the model. We demonstrate how agreement can be assessed between subgroups of the raters and/or subjects, for example, comparing agreement between experienced and less experienced raters. Simulation studies are carried out to test the performance of the proposed models and measures of agreement. Application to a large-scale breast cancer study is presented.