  ROC analyses are considered under a variety of assumptions concerning the
distributions of a measurement $X$ in two populations. These include the
binormal model as well as nonparametric models where little is assumed about
the form of distributions. The methodology is based on a characterization of
statistical evidence which is dependent on the specification of prior
distributions for the unknown population distributions as well as for the
relevant prevalence $w$ of the disease in a given population. In all cases,
elicitation algorithms are provided to guide the selection of the priors.
Inferences are derived for the AUC as well as the cutoff $c$ used for
classification and the associated error characteristics.
