Agreement Rate Statistics

Posted: April 8, 2021 by Podwits Administrator in Uncategorized

We can now move to completely general formulas for the shares of the general and specific agreement. They apply to binary, orderly or nominal categories and allow for any number of advisors, with a potentially different number of different advisors or councils for each case. There is often an interest in whether the measurements of two different observers (sometimes more than two) or two different techniques yield similar results. This is called concordance or condore or reproducibility between measurements. Such an analysis examines the pairs of measurements, either categorically or numerically both, with each pair being performed on a person (or a pathology slide or an X-ray). As mentioned above, correlation is not synonymous with agreement. The correlation refers to the existence of a relationship between two different variables, while the agreement considers the agreement between two measures of a variable. Two sets of observations, strongly correlated, may have a poor agreement; However, if the two sets of values agree, they will certainly be strongly correlated. For example, in the hemoglobin example, the correlation coefficient between the values of the two methods is high, although the agreement is poor [Figure 2]; (r – 0.98). The other way of looking at it is that, although the different points are not close enough to the dotted line (least square line; [2], indicating a good correlation), these are quite far from the running black line that represents the perfect chord line (Figure 2: the black line running). If there is a good agreement, the dots should fall on or near this line (of the current black line). Step 3: For each pair, put a “1” for the chord and “0” for the chord. For example, participant 4, Judge 1/Judge 2 disagrees (0), Judge 1/Judge 3 disagrees (0) and Judge 2 /Judge 3 agreed (1).

Krippendorffs Alpha[16][17] is a versatile statistic that evaluates the agreement between observers who categorize, evaluate or measure a certain number of objects against the values of a variable. It generalizes several specialized agreement coefficients by accepting any number of observers applicable to nominal, ordinal, interval and proportional levels of measurement, capable of processing missing and corrected data for small sample sizes. Nevertheless, important guidelines have appeared in the literature. Perhaps the first Landis and Koch[13] stated that the values < 0 were unseable and 0-0.20 as light, 0.21-0.40 as just, 0.41-0.60 as moderate, 0.61-0.80 as a substantial agreement and 0.81-1 almost perfect. However, these guidelines are not universally accepted; Landis and Koch did not provide evidence, but relied on personal opinion. It was found that these guidelines could be more harmful than useful. [14] Fleiss`[15]:218 Equally arbitrary guidelines characterize Kappas beyond 0.75 as excellent, 0.40 to 0.75 as just to good and less than 0.40 bad. Use the boarding school agreement to evaluate the agreement between two classifications (nominal or ordinal scales). Consider a situation in which we wish to assess the consistency between hemoglobin measurements (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 these data shows the difference between the two methods for each person [Figure 1]. The average difference between values is 1.07 g/dL (with a standard deviation of 0.36 g/dL) and 95% agreements are 0.35 to 1.79. This implies that the hemoglobin level of a given person, as measured by photometry, could vary from 0.35 g/dL greater than 1.79 g/dL (this is the case for 95% of people; for 5% of people, differences could be outside these limits). This of course means that the two techniques cannot be used as a substitute for each other.

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