PMC:7386785 / 60630-62686 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7386785","sourcedb":"PMC","sourceid":"7386785","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7386785","text":"Some of our findings are counterintuitive, for example that the majority of the studies that investigated cough found that cough decreases the probability of COVID‐19 despite the fact that it is part of the case definition of COVID‐19 in most countries. This is also the case for fever in two studies and myalgia in one study ‐ even though these features were also red flags in at least one other study. We believe this may be caused by selection bias. Selection bias is present when selective and non‐random inclusion and exclusion of participants apply and the resulting association between exposure and outcome (here the accuracy of the test) differs in the selected study population compared to the eligible study population, and it has been shown that this may decrease estimates of diagnostic accuracy (Rutjes 2006). For the diagnosis of COVID‐19, rapidly and constantly changing, and widely variable test criteria have influenced who was referred for testing and who was not. Inclusion in the study of only a selective fraction of eligible patients can give a biased estimate of the real accuracy of the index test when measured against the reference standard and real disease status. Griffith 2020 reported on the problematic presence of collider stratification bias in the published studies on COVID‐19. Appropriate sampling strategies need to be applied to avoid conclusions of spurious relationships, more specifically in our case, the biased accuracy estimates of signs and symptoms for the diagnosis of COVID‐19 disease. Selection of patients based on the presence of specific pre‐set symptoms, such as fever and cough, lead to biased associations between these symptoms and disease, and sensitivity and specificity estimates that differ from their true values. The example of collider bias for cough is illustrated in Figure 17. Grouping studies by diagnostic criteria for selection might clarify this issue, but studies do not clearly describe them, with study authors referring to the guidelines in general that were applicable at the 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