Bland JM, DG Altman. (1999) Measurement agreement in comparative study of methods. Statistical methods in medical research 8, 135-160. (7.8 mmol/L is the average glucose level and is subtracted to make the terms square glucose and glucose undecorated.) The square term is statistically significant (P-0.03). We can calculate the absolute remnants of this model and fall back on the average glucose, as before: we could use these regression equations, to estimate the 95% compliance limit, as has been the case so far: despite improved data adaptation, the increased difficulty of using curved limit values makes linear limits, from 2.0 to 0.4 × glucose to 1.8 mmol/L, a more practical estimate of the limits of 95% for the difference between capillaries and plasma population. 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. Specifically, the method provides an estimate of the interval between some of the differences between the measurements. It is used if you are interested in trying a new technique or measurement method that has advantages over what is currently used; It could be easier to use, or cheaper. However, it may also have inconclusive data on its reliability. A Bland-Altman plot (differential diagram) in analytical chemistry or biomedicine is a method of data representation used in the analysis of the agreement 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] If we estimate the approximate limits of 95% of the agreement that ignore this relationship, we have the average difference – 0.3625 mmol/L, SD – 12357 mmol/L We can represent these limits on the difference from the average graph: the limits of concordance estimate the interval in which a share of the differences between the measures is.

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