Conversely, if two tests disagree, one test is likely to be better than the other although there may no way to tell which is better! The exception to this is where both tests have close to 100% specificity (i.e. no or few false positives). In this case the test with the larger number of positive results is likely to be more sensitive. McNemar’s Chi-squared test for paired data can also be used to test for significant differences between the discordant cells (b & c).
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Conversely, if two tests disagree, one test is likely to be better than the other although there may no way to tell which is better! The exception to this is where both tests have close to 100% specificity (i.e. no or few false positives). In this case the test with the larger number of positive results is likely to be more sensitive. McNemar's Chi-squared test for paired data can also be used to test for significant differences between the discordant cells (b & c).
Revisi terkini pada 10 Mei 2015 14.44
Conversely, if two tests disagree, one test is likely to be better than the other although there may no way to tell which is better! The exception to this is where both tests have close to 100% specificity (i.e. no or few false positives). In this case the test with the larger number of positive results is likely to be more sensitive. McNemar's Chi-squared test for paired data can also be used to test for significant differences between the discordant cells (b & c).
