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The FP assay data were subjected to ROC curve analysis which estimates the sensitivity and specificity of a test at every possible cutoff point and provides a measure of test accuracy.
This contrasts with the common practice of estimating the sensitivity and specificity of a test by comparing its results with those obtained by microscopy [13], [14], an inappropriate reference [7], [8].
However, there is an inherent, usually divergent balance between the sensitivity and specificity of a test.
The sensitivity and specificity of a test can, however, vary in populations with different disease spectrums.
However, the use of a certain cut-off score critically relates to the sensitivity and specificity of a test.
They provide estimates of the true positive rate (sensitivity) and true negative rate (specificity) of a test [ 13].
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Good sensitivities and specificities of a test can be achieved only with a large sample size.
By contrast, since 30%40%% of the general population carry such HLA alleles, the specificity of such a test is very low.
Including additional biomarkers might improve the specificity of such a test.
ROC curves are graphical visualizations of the reciprocal relation between sensitivity (Se) and specificity (Sp) of a test for various values.
Sample calculations to show how such errors in a reference test lead to alterations in the apparent sensitivity and specificity of a diagnostic test are presented in Appendix 1 and 2 (see Additional file 2: Appendices).
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