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We considered a difference of more than one category between two tests as clinically meaningful.
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Percent discordance between two tests was calculated as the fraction of total paired observations for which the two methods were discordant among all paired observations.
The level of agreement between two tests was calculated as: (a + d)/ n, where a is the number of samples positive both by PCR and by culture, d is the number of samples negative by both methods and n is the total number of samples under examination [ 44, 45].
According to standard criteria, the concordance between two tests compared is classified as a very good 1-0.76 1-0.76ood (0.75-0.61), c acceptable (0.6-0.4), d poor (< 0.4).
While there was significant discordance (McNemar's test: p = 0.02) between the two tests as shown in figure 1, the overall agreement in TB cases was 69.3% (concordance, κ = 0.27).
We recruited children who were treated for intellectual disability at a teaching hospital and had two IQ tests from 2001 to 2005 and used the difference between the two tests as the indicator of effectiveness.
Each choice between two tests and the opt-out was considered as a specific observation.
* kappa = measure of agreement between two tests.
In their study, the similarity degree (d s d ) between two test cases was calculated as the number of identical transitions (nit) divided by the average test case length.
As shown in Table 2, an overall similarity between two test systems was 98.4% (P >0.05).
Despite all precautions to ensure independence between the two tests in selected studies (such as different providers, and blinding of providers towards each test), some level of contamination cannot be eliminated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com