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Human thermal hyperpneae was shown for the first time that reproducible in humans during training that gave actively induced hyperthermia (Sancheti and White, 2006). Each of the core temperature thresholds for respiratory equivalents for oxygen and carbon dioxide, as well as the high core temperature point for tidal volume and the frequency of temperature at the core of respiratory reactions had high and significant internal intraclass correlation coefficients (0.88 < r < 0.93; p < 0.05). The Bland-Altman plots (Bland and Altman, 1986) also showed random differences in experimentation in these thresholds, which did not differ significantly from zero. Learn how to evaluate the match between two measurement methods using it analytics for Microsoft Excel. 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. An example of linear regression analysis and Bland-Altman plots to compare the two values represented in Table 8.1 is shown in Figure 8.6. One of the main applications of the Bland-Altman plot is to compare two clinical measurements, each of which has produced an error in its measurements. [5] It can also be used to compare a new technique or measurement method with a gold standard, because even a gold standard does not imply it without error – and should not involve it. [4] Software that provides Bland Altman plots is available on Analysis-it, MedCalc, NCSS, GraphPad Prism, R or StatsDirect.
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.