The results were mixed: For instance, Cameron (2002) did not find significant correlations between the two test types, while Mochida and Harrington (2006) reported high correlations of above.80.80
We found substantial evidence for the ability of all markers to reduce collisions (~ 57% reduction), with little difference between the tested marker types.
A final serostatus (negative, acute, or past infection) was assigned to each sample on the basis of the combined results of all three assay sets, as defined by agreement between at least two of the three test types.
The analytical strategy thus compared the test accuracy of commercial brands within each test type before making comparisons between test types.
There was a statistically significant (P = 0.008) difference in accuracy between test types (Table 5) with Type 4 tests being significantly more sensitive than Type 2 (P = 0.01) and Type 3 (P = 0.03) (Table 6) based on indirect comparisons using all available data.
The bias between candidates and test type indicated that the candidates' ability varied across test type and across test items.
A significant interaction effect is observed between the test results and attribution type in the public context (F 1, 12) = 7.94, p < 0.05) and in the private context (F 1, 12) = 5.68, p < 0.05).
Finally, the problem of cloud type identification for the test sample was solved according to the similarity between the test sample and specific cloud type subspace.
AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison.
Finally, the similarity score between the test sample and the cancer type was calculated by taking a mean of the ts-scores.
