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Affinity-based measures of biomarker performance evaluation.

Miguel de Carvalho1, Bradley J Barney2, Garritt L Page3

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

Statistical Methods in Medical Research
|May 11, 2019
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Summary
This summary is machine-generated.

We introduce novel summary measures for biomarker accuracy, complementing existing diagnostic tools. These new measures, akin to Hellinger affinity, offer enhanced agreement assessment and conditional performance evaluation for biomarkers.

Keywords:
BiomarkerHellinger affinitycovariate-specific diagnosticsummary measure

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Biomarker Research

Background:

  • Existing diagnostic accuracy measures have limitations.
  • There is a need for new summary statistics to evaluate biomarker performance.
  • Biomarker accuracy assessment is crucial in medical research and clinical practice.

Purpose of the Study:

  • To propose novel summary measures of biomarker accuracy.
  • To develop a covariate-specific version for assessing conditional discrimination performance.
  • To provide nonparametric Bayes estimators for these new indexes.

Main Methods:

  • Conceptualization of summary measures based on Hellinger affinity and geometrical principles similar to Pearson correlation.
  • Development of a covariate-specific summary index.
  • Devising nonparametric Bayes estimators and deriving theoretical properties of priors.
  • Performance assessment through a simulation study.

Main Results:

  • The proposed summary measures offer a new way to assess biomarker accuracy.
  • The covariate-specific index allows for conditional performance evaluation.
  • Nonparametric Bayes estimators were successfully devised and their properties derived.
  • Simulation studies confirmed the performance of the proposed methods.

Conclusions:

  • The new summary measures provide valuable companions to existing diagnostic accuracy measures.
  • The covariate-specific index enhances the assessment of biomarker discrimination performance.
  • The proposed statistical methods are robust and applicable in practice.
  • The methods are illustrated effectively using prostate cancer diagnosis data.