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## Overall Agreement Rate

There must be K-rated cases indicated by k -1, …, K. The evaluations made in case k are summarized as follows: njk (j 1, …, C) – n1k, n2k, …, nck. If advisors tend to accept, the differences between advisors` comments will be close to zero. If one advisor is generally higher or lower than the other by a consistent amount, the distortion differs from zero. If advisors tend to disagree, but without a consistent model of one assessment above each other, the average will be close to zero. Confidence limits (generally 95%) It is possible to calculate for bias and for each of the limits of the agreement. Two methods are available to assess the consistency between continuously measuring a variable on observers, instruments, dates, etc. One of them, the intraclass coefficient correlation coefficient (CCI), provides a single measure of the magnitude of the match and the other, the Bland-Altman diagram, also provides a quantitative estimate of the narrowness of the values of two measures. Kappa is similar to a correlation coefficient, as it can`t exceed 1.0 or -1.0.

Because it is used as a measure of compliance, only positive values are expected in most situations; Negative values would indicate a systematic disagreement. Kappa can only reach very high values if the two matches are good and the target condition rate is close to 50% (because it incorporates the base rate in the calculation of joint probabilities). Several authorities have proposed “thumb rules” to interpret the degree of the agreement, many of which coincide at the center, although the words are not identical.     Statistical meaning. In examining the importance of in, the zero hypothesis is that the spleens are independent, with their probability of assigning borders corresponding to the observed border proportions. For a table 2×2, the test is the usual statistical independence test in an emergency table. In statistics, reliability between boards (also cited under different similar names, such as the inter-rater agreement. B, inter-rated matching, reliability between observers, etc.) is the degree of agreement between the advisors. This is an assessment of the amount of homogeneity or consensus given in the evaluations of different judges. For the three situations described in Table 1, the use of the McNemar test (designed to compare coupled categorical data) would not make a difference. However, this cannot be construed as evidence of an agreement.

The McNemar test compares the total proportions; Therefore, any situation in which the total share of the two examiners in Pass/Fail (for example. B situations 1, 2 and 3 in Table 1) would result in a lack of differences. Similarly, the mated t-test compares the average difference between two observations in a single group. It cannot therefore be significant if the average difference between unit values is small, although the differences between two observers are important for individuals. ( observed agreement [Po] – expected agreement [Pe]) / (agreement 1 expected [Pe]). By comparing two methods of measurement, it is interesting not only to estimate both the bias and the limits of the agreement between the two methods (interdeccis agreement), but also to evaluate these characteristics for each method itself. It is quite possible that the agreement between two methods is bad simply because one method has broad convergence limits, while the other is narrow.

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