In this scenario, Ground Truth positive patients and negative ground Truth patients are also likely to be categorized by the fake comparator. (A) Comparison without misclassification that constitutes perfectly the fundamental truth for 100 negative patients and 100 positive patients. (B) Apparent performance of the diagnostic test based on the misclassification rate of the comparison. Error bars describe 95% of empirical confidence intervals on median, calculated over 100 simulation cycles. Actual test power is displayed when FP and FN rates are 0%, respectively. The concepts of sensitivity and specificity are appropriate if there is no misclassification in comparison (FP rate – FN rate – 0%). The terms „Positive Percent Agreement“ (AAE) and „Negative Percent Agreement“ (NPA) should be used in place of sensitivity or specificity if the comparator is known to contain uncertainty. Figure 4 shows a less idealized diagnostic scenario, in which there is a small degree of horses 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. Because specificity/APA reflects the ability to accurately identify negative controls, which are more widely available than patient samples, IC tends to be narrower for these metrics than in sensitivity/AAE, allowing for consideration of the proportion of positive cases a test can find. 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. The clinical relevance of a test depends on the prevalence of the condition detected. Confidence in a positive or negative outcome in a given clinical population is quantified by the positive forecast value (APP) or negative forecast value (NPV) of a test. The APP and APP of a fixed sensitivity/AAE test and specific changes/NPA based on the prevalence of cases in the population. In the next blog post, we`ll show you how to use Analysis-it to perform the contract test with a treated example.
Since COVID 19`s falsely positive antibody tests could give people a false sense of security that causes them to put themselves in danger and endanger others, the top priority of these tests was to maximize specificity/NPA by minimizing cross-reactivity with other viral proteins.