# Calculate Agreement Between Two Tests

Statistical methods of conformity assessment vary according to the nature of the variables studied and the number of observers between whom the concordance should be assessed. These are summarized in Table 2 and are explained below. If two instruments or techniques are used to measure the same variable on a continuous scale, Bland Altman diagrams can be used to estimate compliance. This diagram is a diagram of the difference between the two measures (Y axis) compared to the average of the two measures (X axis). It therefore offers a graphical representation of the distortion (average difference between the two observers or techniques) with correspondence limits of 95%. The latter are given by the formula: the concordance between the measures refers to the degree of concordance between two (or more) sets of measures. Statistical methods used to verify compliance are used to assess variability between assessors or to decide whether one technique for measuring one variable can replace another. In this article, we look at the statistical concordance levels for different types of data and discuss the differences between these and those that allow correlation to be assessed. Qureshi et al. compared the degree of prostate adenocarnoma assessed by seven pathologists with a standard system (Gleason score). [3] The correspondence between each pathologist and the initial report and between pathologist couples was determined with Cohen`s kappa. That is a useful example. However, we believe that Gleasons Score is an ordinal variable, if weighted kappa would have been a more appropriate choice Dunet V, Klein R, Allenbach G, Renaud J, deKamp R, Prior J.

Quantification of myocardial blood flow by Rb-82 PET/cardiac CT: a detailed reproducibility study between two semi-automatic analysis programs. J Nucl Cardiol. 2015. doi:10.1007/s12350-015-0151-2. Consider a situation in which we would like to evaluate the adequacy between hemoglobin measurements (in g/dl) with a hemoglobinometer on the hospital bed and the formal photometric laboratory technique in ten people [Table 3]. The Bland Altman diagram for this data shows the difference between the two methods for each person [Figure 1]. The mean difference between the values is 1.07 g/dl (with a standard deviation of 0.36 g/dL) and the 95% match limits are 0.35 to 1.79. This means that the hemoglobin level measured by a given person`s photometry can vary from 0.35 g/dl greater than 1.79 g/dl measured by photometry (this is the case for 95% of people; for 5% of individuals, variations could be outside these limits). This obviously means that the two techniques cannot be used as substitutes. It is important that there is no single criterion for acceptable compliance limits; This is a clinical decision that depends on the variables to be measured. Another method of visually assessing the conformity of two tests is to establish a scatter plot of the results of the first test against the results of the second test.

If the two tests have a good match, we should expect the points to fall on or near the 45° line (i.e. y = x); Deviations from this line would indicate a mismatch. Although the pearson correlation coefficient ρ can be used to assess the strength of a linear relationship between the results of two tests, the Pearson correlation is not an appropriate way to assess conformity: while it is true that test results that match well have a strong pearson correlation, the opposite is not always true, as will be illustrated later…

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