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A Tutorial of Bland Altman Analysis in A Bayesian Framework by Krissina M. Alari; Steven B. Kim; Jeffrey O. Wand is a Medicine article available to read on EtoBox.

What is A Tutorial of Bland Altman Analysis in A Bayesian Framework about?

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.

Who reads A Tutorial of Bland Altman Analysis in A Bayesian Framework?

Author

Krissina M. Alari; Steven B. Kim; Jeffrey O. Wand

Publisher

Informa UK Limited

Published

2020

Language

EN

Field

Medicine (Health Sciences)