Statistics How To

Limits of Agreement: Definition

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The limits of agreement approach analyzes:

  • The way in which two ways of taking measurements agree,
  • The extent to which they are compatible.

More specifically, the method gives an estimate of the interval where a proportion of the differences lie between measurements. It is used when you are interested in trying a new measuring technique or method that has advantages to what is currently used; It could be easier to use, or less expensive. However, it might also have inconclusive data available as to its reliability.

The limits of agreement approach was introduced by English statisticians Martin Bland and Douglas Altman in 1983. The method became popular after the authors’ 1986 article in The Lancet. This second article is one of the most frequently cited statistics articles, having been cited more than 30,000 times.

Analyzing Data with the Bland Altman Limits of Agreement Method

To compare measuring systems using the Bland Altman method, the differences between individual measurements taken by the two different measuring systems are calculated, and then the mean and standard deviation is derived. The 95 percent ‘limits of agreement’ is calculated as the mean of the two values, minus and plus 1.96 standard deviations. This 95 percent limits of agreement should contain the difference between the two measuring systems for 95 percent of future measurement pairs.

The Bland Altman plot, also known as the difference plot, is a graphical way of doing this where the differences between two measuring systems are plotted against the averages of the two.

References

Myles & Cui. Using the Bland–Altman method to measure agreement with repeated measures. BJA: British Journal of Anaesthesia, Volume 99, Issue 3, 1 September 2007, Pages 309–311, https://doi.org/10.1093/bja/aem214. Retrieved from https://academic.oup.com/bja/article/99/3/309/355972 on April 23, 2018

Bland, J. Martin and Altman, Douglas G. (2007) Agreement between methods of measurement with multiple observations per individua17 (4). 571-582. Retrieved May 13, 2018 from: https://www.ncbi.nlm.nih.gov/pubmed/17613642

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