A Tutorial of Bland Altman Analysis in A Bayesian Framework. Issue 2 (3rd April 2021)

• Record Type: Journal Article
• Authors: Alari, Krissina M.; Kim, Steven B.; Wand, Jeffrey O.
• Is Part Of: Measurement in physical education and exercise science. Volume 25:Issue 2(2021)
• Page Start: 137
• Page End: 148
• Publication Date: 2021-04-03
• Subjects: Bland Altman analysis -- reliability study -- Bayesian inference -- posterior predictive distribution -- informative prior; Physical fitness -- Testing -- Periodicals; Physical education and training -- Statistical methods -- Periodicals; Physical education and training -- Research -- Periodicals; 613.7
• DOI: 10.1080/1091367X.2020.1853130
• Languages: English
• ISSNs: 1091-367X

Abstract

ABSTRACT: There are two schools of thought in statistical analysis, frequentist, and Bayesian. Though the two approaches produce similar estimations and predictions in large-sample studies, their interpretations are different. Bland Altman analysis is a statistical method that is widely used for comparing two methods of measurement. It was originally proposed under a frequentist framework, and it has not been used under a Bayesian framework despite the growing popularity of Bayesian analysis. It seems that the mathematical and computational complexity narrows access to Bayesian Bland Altman analysis. In this article, we provide a tutorial of Bayesian Bland Altman analysis. One approach we suggest is to address the objective of Bland Altman analysis via the posterior predictive distribution. We can estimate the probability of an acceptable degree of disagreement (fixed a priori ) for the difference between two future measurements. To ease mathematical and computational complexity, an interface applet is provided with a guideline.