  We propose new summary measures of diagnostic test accuracy which can be used
as companions to existing diagnostic accuracy measures. Conceptually, our
summary measures are tantamount to the so-called Hellinger affinity and we show
that they can be regarded as measures of agreement constructed from similar
geometrical principles as Pearson correlation. A covariate-specific version of
our summary index is developed, which can be used to assess the discrimination
performance of a diagnostic test, conditionally on the value of a predictor.
Nonparametric Bayes estimators for the proposed indexes are devised,
theoretical properties of the corresponding priors are derived, and the
performance of our methods is assessed through a simulation study. Data from a
prostate cancer diagnosis study are used to illustrate our methods.
