Bayesian probability of agreement for comparing the similarity of response surfaces

Journal Article · Mon Apr 08 00:00:00 EDT 2019 · Journal of Quality Technology

DOI:https://doi.org/10.1080/00224065.2019.1569961· OSTI ID:1514939

Stevens, Nathaniel T. ^[1]; Rigdon, Steven E. ^[2]; Anderson-Cook, Christine M. ^[3]

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1. Univ. of San Francisco, CA (United States)
2. Saint Louis Univ., MO (United States)
3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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, 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.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1514939
• Report Number(s): LA-UR--17-24158
• Journal Information: Journal of Quality Technology, Journal Name: Journal of Quality Technology Journal Issue: 1 Vol. 52; ISSN 0022-4065
• Publisher: American Society for Quality (ASQ)
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Markov chain Monte Carlo
Poisson regression
comparing subpopulations
equivalence testing
linear regression
logistic regression