A Tutorial of Bland Altman Analysis in a Bayesian Framework

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.

Krissi Alari

Statistics Graduate Student

My interests include applications of Bayesian statistics, generalized linear models, machine learning, and data visualization.