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Bayesian inference for agreement measures.

Ignacio Vidal1, Mário de Castro2

  • 1a Instituto de Matemática y Física, Universidad de Talca , Talca , Chile.

Journal of Biopharmaceutical Statistics
|August 27, 2016
PubMed
Summary
This summary is machine-generated.

This study presents a simple Bayesian method for assessing agreement between measurement methods, avoiding complex simulations. The approach is effective across various prior distributions and applicable in medicine, metrology, and engineering.

Keywords:
Accuracyconcordance correlation coefficientcoverage probabilityprecisiontotal deviation index

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Area of Science:

  • Statistics
  • Bayesian Inference
  • Measurement Science

Background:

  • Assessing agreement between measurement methods is crucial in Medicine, Metrology, and Engineering.
  • Existing agreement measures are analyzed from a Bayesian perspective.
  • Bayesian inference procedures for the bivariate normal distribution are foundational.

Purpose of the Study:

  • To develop a general, simple, and effective Bayesian method for agreement analysis.
  • To provide posterior inferences for agreement measures without relying on Markov Chain Monte Carlo (MCMC) methods.
  • To offer a flexible approach applicable with diverse prior distributions.

Main Methods:

  • Bayesian inference for bivariate normal distribution.
  • Development of a novel agreement assessment method.
  • Utilizing five objective priors for the bivariate normal distribution.

Main Results:

  • A general, simple, and effective Bayesian method for agreement analysis was developed.
  • The method does not require MCMC, offering computational efficiency.
  • Model adequacy assessment tools and a real-data application are presented.

Conclusions:

  • The proposed Bayesian method provides a straightforward and versatile approach to evaluating measurement agreement.
  • It is applicable across various disciplines and prior specifications.
  • The method is validated through simulation studies and a real-world dataset analysis.