Figure 3 shows the effects of falsely positive and falsely negative comparison errors on the apparent performance of a perfect test. In this simulation, there is no overlap between negative Truth ground and Ground Truth positive patients. The test is accepted 100% accurately, so that the reduced test performance values shown below different comparison default rates are purely the result of uncertainty in the reference value. Differences between 0 and 20% in the non-ranking rate of comparisons result in a monotonous decline in CSAs and other performance indicators. Figure 3 also shows that the decrease in apparent test power observed due to comparison noises can be expressed in relation to the maximum possible test power, in the absence of comparison noises. As more and more people are exposed to COVID-19 and effective vaccines are being put online, the prevalence of SARS-CoV-2 antibodies will increase in the population, making positive individual test results more trustworthy. Bayes` theorem limits the accuracy of screening tests based on the prevalence of the disease or the likelihood of pre-testing. It has been shown that a test system can tolerate significant decreases in prevalence, to some extent well defined, known as the prevalence threshold, below which the reliability of a positive test drops abruptly. However, Balayla et al.  have shown that sequential tests over the aforementioned Bavarian borders and thus improve the reliability of screening tests. Figure 4 shows a less idealized diagnostic scenario, in which there is a small degree of overlap between positive Ground Truth and ground Truth positive patients.
We look at such a typical high performance test and appreciate the deterioration of apparent test power under increasing uncertainty conditions. Panel A shows the distribution of test results against soil truth. Panel B shows the expected decrease in all test performance parameters as a monotonous function of increasing comparison uncertainty. Note the generally worse apparent test performance of Figure 4 at all levels of comparative classification compared to Figure 3, where ground Truth negative and Ground Truth positive patients do not overlap in diagnostic test results. if a “true positive” is the event that the test makes a positive prediction, and that the subject has a positive result under the gold standard, and a “false positive” is the event that the test makes a positive prediction, and that the subject has a negative result under the gold standard. The ideal value of the APP, with a perfect test, is 1 (100%), and the worst possible value would be zero. The FDA has issued nine COVID-19 antibody tests for Emergency Use Authorization (EEA). The application document (IFU) for each test indicates its sensitivity and specificity in the form of a positive percentage agreement (AEA) or a negative percentage agreement (NPA) with a chain reaction test by reverse transcription polymeraosis (RT-PCR) and 95% confidence intervals (IC) for each value. An objective definition of sensitivity and specificity requires a generally accepted standard of reference, defined as the best available method for determining the existence or absence of a target condition.
A comparator may have an intrinsic property or boundary that allows it to render a broad distribution of values relative to the truth ground that is measured. An example is shown in Figure 1. Suppose a particular condition is characterized by a variable with a continuous normal distribution at the ground truth level, and that cutoffs have been defined to identify rare events (positive or negative calls) at the tail of the distribution.