Affinity-based measures of biomarker performance evaluation

Abstract

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.

Bayesian nonparametric inference for the overlap coefficient: With an application to disease diagnosis

“…14 On the other hand, the coefficient of overlap, because it is not based on any classification rule, would correctly recognize that test outcomes of both populations are perfectly separated. At this point, it is fair to mention the work of Martinez-Camblor et al 15 and of de Carvalho et al, 16 who proposed, respectively, a generalization of the receiver operating characteristic curve and an affinity based measure of diagnostic accuracy that also provide meaningful values in such a context. Summing up, the coefficient of overlap is a versatile measure that (i) has an appealing graphical interpretation, and (ii) does not rely on any classification rule.…”