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Measure Agreement Between Two Raters

By Zach Arnold | April 10, 2021

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On the surface, these data appear to be available for analysis using methods for 2 × 2 tables (if the variable is classified) or correlation (if numerical) that we have previously explained in this series. [1.2] However, further examination would show that this is not true. In these two methods, the two measures relate to different variables for each individual (for example. B exposure and result, height and weight, etc.) whereas, in the `agreement studies`, the two measures refer to the same variable (for example). B, breast x-rays, measured by two radiologists or hemoglobin using two methods). Kappa is a way to measure agreements or reliability and to correct the frequency with which ratings might consent to chance. Cohens Kappa,[5] who works for two councillors, and Fleiss` Kappa,[6] an adaptation that works for any fixed number of councillors, improve the common likelihood that they would take into account the amount of agreement that could be expected by chance. The original versions suffered from the same problem as the probability of joints, as they treat the data as nominal and assume that the evaluations have no natural nature; if the data does have a rank (ordinal measurement value), this information is not fully taken into account in the measurements. Think of two ophthalmologists who measure the pressure of the ophthalmometer with a tonometer.

Each patient therefore has two measures – one of each observer. CCI provides an estimate of the overall agreement between these values. It is akin to a “variance analysis” in that it considers the differences in intermediate pairs expressed as a percentage of the overall variance of the observations (i.e. the overall variability in the “2n” observations, which would be the sum of the differences between pairs and sub-pairs). CCI can take a value of 0 to 1, 0 not agreeing and 1 indicating a perfect match. In statistics, reliability between advisors (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. Another way to conduct reliability tests is the use of the intraclass correlation coefficient (CCI). [12] There are several types, and one is defined as “the percentage of variance of an observation because of the variability between subjects in actual values.” [13] The ICC area can be between 0.0 and 1.0 (an early definition of CCI could be between 1 and 1).

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