Abstract
The quality of medical decision-making and public health planning alike depends directly upon understanding the accuracy of medical tests, especially during a pandemic. But the statistical concepts and measures used to assess test accuracy can be confusing. Why is there not one single definitive measure of test accuracy? How much should individuals worry about spreading COVID-19 if their test results are negative? What do sensitivity, specificity, false positive results, false negative results, and positive predictive value mean relative to each other? In this tutorial, we clarify the meaning of these terms in intuitive ways via visual illustrations, and explain how these terms are all connected to one another through Bayesâ theorem. We show how to use the relationships in that theorem to assess personal risk when large numbers of people are being tested. We illustrate as well the extent to which the accuracy of large numbers of tests depends on the proportion of those tested who have the disease. Overall, we aim to heighten a general intuition regarding the performance of mass medical testing campaigns. Here, toward that end, we review different ways to measure the accuracy of diagnostic tests with reference to pandemic-specific examples.
Highlights
The Vocabulary of OutbreaksThe COVID-19 pandemic caused a massive spike in the broad-scale, government-implemented diagnostic testing of large numbers of people
Why is there not one single definitive measure of test accuracy? How much should individuals worry about spreading COVID-19 if their test results are negative? What do sensitivity, specificity, false positive results, false negative results, and positive predictive value mean relative to each other? In this tutorial, we clarify the meaning of these terms in intuitive ways via visual illustrations, and explain how these terms are all connected to one another through Bayes’ theorem
Prevalence may be larger than incidence as it includes new cases from the past week and individuals who were infected during the prior week and have not yet recovered
Summary
The COVID-19 pandemic caused a massive spike in the broad-scale, government-implemented diagnostic testing of large numbers of people. Less than half of those testing positive will truly have antibodies,’ the CDC said....Alternatively, the same test in a population with an antibody prevalence exceeding 52% will yield a positive predictive value greater than 95%, meaning that less than one in 20 people testing positive will have a false positive test result.” (CNN Wire, 2020.). Both press reports make true but confusing statements, and implicitly prompt questions. We consider the main qualities of an ‘accurate’ test (that is, sensitivity and specificity), and explain how and why these qualities are related
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