Objectives: The objective of this study was to study the impact of ignoring uncertainty by forcing dichotomous classification (presence or absence) of the target disease on estimates of diagnostic... Show moreObjectives: The objective of this study was to study the impact of ignoring uncertainty by forcing dichotomous classification (presence or absence) of the target disease on estimates of diagnostic accuracy of an index test.Study Design and Setting: We evaluated the bias in estimated index test accuracy when forcing an expert panel to make a dichotomous target disease classification for each individual. Data for various scenarios with expert panels were simulated by varying the number and accuracy of "component reference tests" available to the expert panel, index test sensitivity and specificity, and target disease prevalence.Results: Index test accuracy estimates are likely to be biased when there is uncertainty surrounding the presence or absence of the target disease. Direction and amount of bias depend on the number and accuracy of component reference tests, target disease prevalence, and the true values of index test sensitivity and specificity.Conclusion: In this simulation, forcing expert panels to make a dichotomous decision on target disease classification in the presence of uncertainty leads to biased estimates of index test accuracy. Empirical studies are needed to demonstrate whether this bias can be reduced by assigning a probability of target disease presence for each individual, or using advanced statistical methods to account for uncertainty in target disease classification. (C) 2019 Elsevier Inc. All rights reserved. Show less
