Given that differences of opinion on the definition of characteristics and differences of opinion on the width of the evaluation category are different components of differences of opinion, with different practical implications, it is ideally appropriate to have a statistical approach to each quantification of these data. Despite the many obvious options for analyzing contract data, the fundamental questions are very simple. In general, there are one or two methods better for a particular application. However, it is necessary to clearly identify the purpose of the analysis and the substantive issues to be resolved. In other situations, you may want to consider combining the evaluations of two or more ratchets to get assessments of the appropriate accuracy. If so, there are also some methods that are appropriate for this purpose. The question focuses on quantifying the match between the results of two (or more) tests. In other words, both tests should give similar results if applied to the same subject. Here we look at the state of mind in which we have a sample of subjects that have been tested with both tests.

A natural starting point for assessing the consistency between quantitative results would be to take into account differences between test results for each subject. While the coupled t-test could then be used to test whether the average difference is significantly different from zero, this test cannot provide evidence that there is a match. In other words, rejecting the zero hypothesis that there is no difference between the two test results would only allow us to say that the tests do not match; Not rejecting this assumption would not be evidence that the tests are consistent. Chen CC, Barnhart HX. Evaluation of compliance with intraclassade correlation coefficient and correlation coefficient for data with repeated measurements. Comput Stat Data Anal 2013;60:132-45. Kalantri et al. considered the accuracy and reliability of Pallor as a tool for detecting anemia.

[5] They concluded that “clinical evaluation of pallor in cases of severe anaemia may exclude and govern modestly.” However, the inter-observer agreement for pallor detection was very poor (Kappa values -0.07 for conjunctiva pallor and 0.20 for tongue pallor), meaning that pallor is an unreliable sign of diagnosis of anemia. Another important area of research is to propose effective samples to compare the diagnostic test agreement. The selection of sample size and the allocation of sample size to strata require consideration, particularly for strata with rare events where a large number of samples need to be reinted to find a single positive specimen. Another important avenue of research is the effective use of the auxiliary information available for each stratification specimen. The example of an HPV test showed the value of several previous test results to highlight the most informative samples: those that probably do not have consistent results. Such effective sample samples could achieve most of the statistical effectiveness of re-examining all samples, with significant reductions in the cost of studies and consumption of valuable specimens.