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Scientists often use a paired comparison of the areas under the receiveroperating characteristic curves to decide which continuous cancer screeningtest has the best diagnostic accuracy. In the paired design, all participantsare screened with both tests. Participants with unremarkable screening resultsenter a follow-up period. Participants with suspicious screening results andthose who show evidence of disease during follow-up receive the gold standardtest. The remaining participants are classified as non-cases, even though somemay have occult disease. The standard analysis includes all study participantsin the analysis, which can create bias in the estimates of diagnostic accuracy.If the bias affects the area under the curve for one screening test more thanthe other screening test, scientists may make the wrong decision as to whichscreening test has better diagnostic accuracy. We describe a weighted maximumlikelihood bias correction method to reduce decision errors. We assessed theability of the bias correction method to reduce decision errors via simulationstudies. The simulations compared the Type I error rate and power of thestandard analysis with that of the bias-corrected analysis. The performance ofthe bias correction method depends on characteristics of the screening testsand the disease, and on the percentage of study participants who receive thegold standard test. In studies with a large amount of bias in the difference inthe full area under the curve, the bias correction method reduces the Type Ierror rate and improves power for the correct decision. In order to determineif bias correction is needed for a specific screening trial, we recommend theinvestigator conduct a simulation study using our free software.

Reducing decision errors in the paired comparison of the diagnostic accuracy of screening tests with Gaussian outcomes