Affinity-based measures of biomarker performance evaluation

Author:

Carvalho Miguel de1ORCID,Barney Bradley J2,Page Garritt L3ORCID

Affiliation:

1. School of Mathematics, University of Edinburgh, Scotland, UK

2. Department of Pediatrics, University of Utah, Salt Lake City, UT, USA

3. Department of Statistics, Brigham Young University, Provo, UT, USA

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.

Funder

Fundação para a Ciência e a Tecnologia

Publisher

SAGE Publications

Subject

Health Information Management,Statistics and Probability,Epidemiology

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bayesian nonparametric inference for the overlap coefficient: With an application to disease diagnosis;Statistics in Medicine;2022-06-27

2. On Classical Measurement Error within a Bayesian Nonparametric Framework;Springer Proceedings in Mathematics & Statistics;2022

3. Correlation for tree-shaped datasets and its Bayesian estimation;Computational Statistics & Data Analysis;2021-12

4. A non-parametric test for comparing conditional ROC curves;Computational Statistics & Data Analysis;2021-05

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