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Long-term specificity is for "the period thereafter (where the curve is flat)".
A window period is estimated, and then estimates of the sensitivity, short-term specificity and long-term specificity are determined with respect to the window period.
Our analysis shows that the McDougal estimator can be reduced to a formula that only requires calibration of a mean window period and a long-term specificity.
As we have shown, relaxing this assumption means that the long-term specificity becomes epidemic state dependent and hence is time dependent.
In order to calibrate the sensitivity, short-term specificity and long-term specificity, "a plot of the proportion of specimens positive in the assay versus time since seroconversion" is generated (also later referred to as "the curve").
Recently, we have also proposed a formally rigorous incidence paradigm [8], which accounts for assay non-progression using fewer assumptions than are made by McDougal et al. The parameters that emerge naturally in our estimator are a mean window period and a probability of not progressing on the assay (which can also be expressed as a long-term specificity).
Our findings thus confirm the previous results by McDougal et al. [6] and Hargrove et al. [7] that cBED assay-based HIV incidence estimates are not significantly different from longitudinally measured HIV incidence, when a locally calibrated long-term FPR ratio is used to adjust for the imperfect long-term specificity of the cBED assay.
Another limitation is that the correction required the knowledge of long-term specificity.
Effects were corrected for long-term specificity using a previously published formula.
The corrected IRR depends on the long-term specificity, which varies with the window period (see below).
When using the conventional cut-off value for BED, the long-term specificity was 93.6% (1 minus 6.4%).
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