Bayesian probability of agreement for comparing the similarity of response surfaces. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Stevens, Nathaniel T.; Rigdon, Steven E.; Anderson-Cook, Christine M.
• Is Part Of: Journal of quality technology. Volume 52:Issue 1(2020)
• Page Start: 67
• Page End: 80
• Publication Date: 2020-01-02
• Subjects: comparing subpopulations -- equivalence testing -- linear regression -- logistic regression -- Markov chain Monte Carlo -- Poisson regression; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2019.1569961
• Languages: English
• ISSNs: 0022-4065

Abstract

Abstract: For a variety of linear and generalized linear models, there may be interest in evaluating whether the expected response values in two different populations are sufficiently similar to be considered practically equivalent. The probability of agreement has been introduced as a strategy for quantifying the similarity between two groups. We propose a Bayesian version of the probability of agreement to quantify the similarity between linear and generalized linear response surfaces. The proposed methodology is based on Markov chain Monte Carlo estimation and it allows for an intuitive interpretation based directly on population parameters. As demonstrated here, the methodology can be flexibly applied to a variety of different models. We illustrate its use with three examples for which the response depends on predictor variables through a linear, logistic, or Poisson regression model. The computation associated with this approach has been automated with a freely available R Shiny app.