eirasagree, R package

The Bland and Altman plot method is a widely cited and applied graphical approach to assess the equivalence of quantitative measurement techniques, usually aiming to replace a traditional technique with a new, less invasive or less expensive one. However, the Bland and Altman plot is often misinterpreted due to a lack of suitable inferential statistical support. Usual alternatives, such as Pearson's correlation or ordinal least-square linear regression, also fail to identify the weaknesses of each measurement technique. This is a package designed for the analysis of equivalence between measurement techniques. It should be noted that this package does not introduce another iteration of the Bland-Altman plot method. The package's name and our intention were simply inspired by the shared objective of establishing equivalence. This objective revolves around comparing single or repeated interval-scaled measures from two measurement techniques applied to the same subjects. We have developed a completely different inferential test in contrast to the original Bland-Altman proposal. We have highlighted certain criticisms of the original Bland-Altman plot method, which heavily relies on visual inspection and subjectivity for determining equivalence. Our goal is to empower the reader to make an informed decision regarding the validity of this new measurement technique. Here, inferential statistics support for equivalence between measurement techniques is proposed in three nested tests based on structural regressions to assess the equivalence of structural means (accuracy), equivalence of structural variances (precision), and concordance with the structural bisector line (agreement in measurements obtained from the same subject), by analytical methods and robust approach by bootstrapping. Graphical outputs are also implemented to follow Bland and Altman's principles for easy communication. The related publication shows that this approach was tested on five datasets from articles that used Bland and Altman's method. In one case, where authors concluded disagreement, the approach identified equivalence by addressing bias correction. In another case, it aligned with the original assessment but refined the original authors’ results. In a specific case, unnecessary numerical transformations led to a conclusion of equivalence, but this approach, which naturally generates slanted bands, found non-equivalence in precision and agreement. In one case where authors claimed disagreement, the approach revealed precision issues, rendering the comparison invalid.