Affinity-based measures of biomarker performance evaluation

Miguel de Carvalho, Bradley J. Barney, Garritt L. Page

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)837-853
Number of pages17
JournalStatistical Methods in Medical Research
Issue number3
Early online date10 May 2019
Publication statusPublished - 1 Mar 2020


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