07 Abr Agreement Between Two Tests
It is important to note that in each of the three situations in Table 1, the passport percentages are the same for both examiners, and if the two examiners are compared to a typical 2-×-2 test for mated data (McNemar test), there would be no difference between their performance; On the other hand, the agreement between the observers is very different in these three situations. The basic idea that must be understood here is that “agreement” quantifies the agreement between the two examiners for each of the “couples” of the scores, not the similarity of the total pass percentage between the examiners. Very often, contract studies are an indirect attempt to validate a new system or evaluation instrument. In other words, in the absence of a final criterion variable or a “gold standard,” the accuracy of a scale or instrument is assessed by comparing its results when used by different advisors. Here, we can use methods that address the problem of real anxiety – to what extent do ratings reflect the true property we want to measure? Dunet V, Klein R, Allenbach G, Renaud J, deKamp R, Prior J. Myocardal Electrification by Rb-82 Herz PET/CT: A detailed reproducibility study between two semi-automatic analysis programs. J Nucl Cardiol. Doi:10.1007/s12350-015-0151-2. On the other hand, category definitions differ because the spleens divide the characteristic into different intervals. For example, an advisor with a “low skill” may mean themes from the 1st to 20th percentile.
However, another advisor may call it themes from the 1st to 10th percentile. In this case, thresholds for evaluators can generally be adjusted to improve compliance. The similarity of the definitions of the category is reflected as a marginal homogeneity between the advisors. Marginal homogeneity means that the frequencies (or “base rates”), with which two advisors use different rating categories, are identical. Qureshi et al. compared the degree of prostatic adenocarcinoma assessed by seven pathologists using a standard system (Gleason score).  The agreement between each pathologist and the initial relationship and between the pairs of pathologists was determined with Cohen`s Kappa. That is a useful example. However, we think Gleason`s score is an ordinal variable, Kappa weighted might have been a more appropriate choice Chen CC, Barnhart HX. Evaluation of the agreement with repeated measurements for random observers. Stat Med 2011;30:3546-59.
(5) Data that appear to be in a bad line can lead to fairly high correlations. For example, Serfontein and Jaroszewicz  compared two methods of measuring the age of pregnancy. Babies with a pregnancy age of 35 weeks by one method had plagues between 34 and 39.5 weeks per other, but r was high (0.85). On the other hand, Oldham et al.  compared Wright mini-and large flowmeters and found a correlation of 0.992. They then connected the meters in series, so that both measured the same throughput and achieved a “material improvement” (0.996). If a correlation coefficient of 0.99 can be significantly improved, we need to reconsider our view of a strong correlation in this context. As we show below, the strong correlation of 0.94 for our own data masks an important disagreement between the two instruments. The Intraclassical Correlation Coefficient (CCI) is an alternative to the Pearson correlation, which is more suited to comparing diagnostic tests. It was first proposed by Fisher4 and is defined assuming that the results of diagnostic tests follow a unilateral ANOVA model with a random effect on the object. This random effect takes into account the repeated measurements for each subject.