To compare the Bland Altman measurement systems, the differences between the different measurements of the two different measurement systems are calculated and the average and the standard deviation are calculated. The 95% of “agreement limits” are calculated as the average of the two values minus and plus 1.96 standard deviation. This 95 per cent agreement limit should include the difference between the two measurement systems for 95 per cent of future measurement pairs. Bland and Altman indicate that two measurement methods developed to measure the same parameter (or property) should have a good correlation when a group of samples is selected so that the property to be determined varies 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 compliance limits for the two harness measures were -0.4 mm and 0.42 mm. Bland-Altman plots are widely used to assess the agreement between two 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.