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Objective: Bland and Altman plot method is a widely cited and appliedgraphical approach for assessing the equivalence of quantitative measurementtechniques, usually aiming to replace a traditional technique with a new, lessinvasive, or less expensive one. Although easy to communicate, Bland and Altmanplot is often misinterpreted by lacking suitable inferential statisticalsupport. Usual alternatives, such as Pearson's correlation or ordinalleast-square linear regression, also fail to locate the weakness of eachmeasurement technique. Method: Here, inferential statistics support forequivalence between measurement techniques is proposed in three nested testsbased on structural regressions to assess the equivalence of structural means(accuracy), the equivalence of structural variances (precision), andconcordance with the structural bisector line (agreement in measurementsobtained from the same subject), by analytical methods and robust approach bybootstrapping. Graphical outputs are also implemented to follow Bland andAltman's principles for easy communication. Results: The performance of thismethod is shown and confronted with five data sets from previously publishedarticles that applied Bland and Altman's method. One case demonstrated strictequivalence, three cases showed partial equivalence, and one showed poorequivalence. The developed R package containing open codes and data areavailable with installation instructions for free distribution at HarvardDataverse at https://doi.org/10.7910/DVN/AGJPZH. It is possible to test whethertwo techniques may have full equivalence, preserving graphical communicationaccording to Bland and Altman's principles, but adding robust and suitableinferential statistics. Decomposing the equivalence in accuracy, precision, andagreement helps the location of the source of the problem in order to fix a newtechnique.

Is Bland-Altman plot method useful without inference for accuracy, precision, and agreement?