Gwet KL (2016) Review of the difference in agreement coefficients correlated with statistical significance. Educ Psychol Meas 76 (4): 609-637 Natural heterogeneity among the test results of the subjects is reflected in the random effect variance of subject 2u. Subjects with clearly defined pathologies have a higher random effect and are more often classified by experts in a higher category of diseases, with greater agreement among experts; Lower ui values are observed in test results with less clearly defined disease status. The variance in random effects of experts is higher for a more heterogeneous group of experts; an expert who generously assigns (economy) high categories of disease has a higher positive (negative) value of vj; Experts who are not overly liberal or conservative in their ratings will have more modest values of vj. The proposed model also allows for possible interactions between the notions of random effects of expert and subject, as shown by an alternative derivative of the model (Annex 2, Complementary Materials). Tables showing a pair match between a few randomly selected pairs of urologists ranking 46 slides [46] on an ordination scale based on Gleason graduation scores with C-4 categories. with the probability that the test result of the expert j subject in category c will be classified pre (Yij – c∣i, vj), . . It is useful to define a random Q variable with a logistic distribution with the average 0 and 3 variance and the density function fQ (q) – e-q (1 e-q) 2. The observed agreement p0 is derived for Ordinal-GLMM LOGISTIC as: Since it is a classification measure and generally practitioners generally rank a small number of items, its application cases are usually smaller matrixes. This is because it is difficult for human advisors to be precise when too many options are presented that must be categorized by a certain criterion.

Particularly in small settings, the proposed amount for pardS measurement is calculated quickly and looks promising because it is used for each data point and can be interpreted directly, as it is a cumulative relative frequency that is used as an estimate of cumulative probability. The sum of the PARD approach is the only interrated agreement measure represented by a probability. Random agreement is the proportion of time that experts agree on their classifications only by chance. The true measure of randomly predicted match is pc the probability that two randomly selected subjects i and i” (i ≠ i) are given by two randomly selected experts j and j” (j ≠ j) on the basis of an ordinal classification scale with categories C (1): Cohen J (1960) A coefficient of agreement for nominal balances. Educ Psychol Meas 20 (1): 37-46 If k numbers are assigned unreco replaced to k seats, there is usually k! Ways to fix them. That means there are k`s! different options to create a matrix line. In addition, the number of possible dies is the number of possible dies in a position for setting the time “k” k! .., attitude. With this information, it is possible to determine the sum of a sum of PARds from zero. As a sum of PARDs of zero would mean that the rankings are the same and that the lines must be the same, there is k! The possibilities of achieving what is called a zero difference. Therefore, the probability of a zero difference is given by: . (P (d_{0}) “frac`k!` “K!” The zero difference is a reference for the whole process, as it refers to an attitude in which all advisors agree on all classification decisions (global agreement).