Affinity-based measures of biomarker performance evaluation.

We propose new summary measures of biomarker 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. We develop a covariate-specific version of our summary index, which practitioners can use to assess the discrimination performance of a biomarker, conditionally on the value of a predictor. We devise nonparametric Bayes estimators for the proposed indexes, derive theoretical properties of the corresponding priors, and assess the performance of our methods through a simulation study. The proposed methods are illustrated using data from a prostate cancer diagnosis study.