Affinity-based measures of biomarker performance evaluation. (March 2020)

• Record Type: Journal Article
• Authors: Carvalho, Miguel de; Barney, Bradley J; Page, Garritt L
• Other Names: Austin Peter guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 29:Number 3(2020)
• Page Start: 837
• Page End: 853
• Publication Date: 2020-03
• Subjects: Biomarker -- covariate-specific diagnostic -- Hellinger affinity -- summary measure; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219846157
• Languages: English
• ISSNs: 0962-2802

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.