Learn how to evaluate the match between two measurement methods using it analytics for Microsoft Excel. The tutorial discusses the relationship between methods, estimating average distortion and match limits, understanding the importance of repeatability, using replication measures, managing a relationship between difference and size, transforming measures to suppress a relationship, estimating regression-based compliance limits, and estimating non-parametric compliance limits. Bland-Altman parcels were also used to investigate a possible link between the differences between the measurements and the actual value (i.e. proportional distortion). The existence of proportional distortion indicates that the methods do not uniformly correspond to the range of measures (i.e., the limits of compliance depend on the actual measure). To formally assess this relationship, the difference between methods should be reduced to the average of the two methods. If a relationship between differences and actual value has been identified (i.e. a significant slope of the regression line), 95% regression-based agreements should be indicated. [4] A Bland-Altman plot (differential diagram) in analytical chemistry or biomedicine is a method of data representation used in the analysis of concordance between two different trials.
It is identical to a tube of average difference Tukey,[1] the name under which it is known in other areas, but it was popularized in the medical statistics of J. Martin Bland and Douglas G. Altman. [2] [3] Bland and Altman indicate that two methods developed to measure the same parameter (or property) should have a good correlation when a group of samples is selected in such a way as to vary the property to be determined considerably. Therefore, a high correlation for two methods of measuring the same property could in itself be only a sign that a widely used sample has been chosen. A high correlation does not necessarily mean that there is a good agreement between the two methods. The difference diagram shows the average distortion estimated using a linear regression. Concordance limits are estimated from the residual standard deviation of regression.
A similar method was proposed by Eksborg in 1981. [7] This method was based on the regression of Deming , a method introduced by Adcock in 1878. To compare the differences between the two samples, regardless of their averages, it is best to consider the ratio between the pairs of measurements. [4] The log transformation (base 2) of the measurements prior to the analysis makes it possible to use the standard approach; thus the action will be given by the following equation: The Bland-Altman plots are widely used to evaluate the agreement between two different instruments or two measurement techniques. Bland-Altman plots identify systematic differences between measures (i.e. fixed pre-stress) or potential outliers. The average difference is the estimated distortion, and the SD of the differences measures random fluctuations around this average. If the average value of the difference based on a 1-sample-t test deviates significantly from 0, this means the presence of a solid distortion. If there is a consistent distortion, it can be adjusted by subtracting the average difference from the new method. It is customary to calculate compliance limits of 95% for each comparison (average difference ± 1.96 standard deviation of the difference), which tells us how much the measurements were more likely in two methods for most people. If the differences in the average± 1.96 SD are not clinically important, the two methods can be interchangeable.
The 95% agreement limits can be unreliable estimates of population parameters, especially for small sampling sizes, so it is important to calculate confidence intervals for 95% compliance limits when comparing methods or evaluating repeatability. This can be done by the approximate Bland and Altman method [3] or by more precise methods. [6] Sometimes a transformation does not solve
