  Scientists often use a paired comparison of the areas under the receiver
operating characteristic curves to decide which continuous cancer screening
test has the best diagnostic accuracy. In the paired design, all participants
are screened with both tests. Participants with unremarkable screening results
enter a follow-up period. Participants with suspicious screening results and
those who show evidence of disease during follow-up receive the gold standard
test. The remaining participants are classified as non-cases, even though some
may have occult disease. The standard analysis includes all study participants
in 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 than
the other screening test, scientists may make the wrong decision as to which
screening test has better diagnostic accuracy. We describe a weighted maximum
likelihood bias correction method to reduce decision errors. We assessed the
ability of the bias correction method to reduce decision errors via simulation
studies. The simulations compared the Type I error rate and power of the
standard analysis with that of the bias-corrected analysis. The performance of
the bias correction method depends on characteristics of the screening tests
and the disease, and on the percentage of study participants who receive the
gold standard test. In studies with a large amount of bias in the difference in
the full area under the curve, the bias correction method reduces the Type I
error rate and improves power for the correct decision. In order to determine
if bias correction is needed for a specific screening trial, we recommend the
investigator conduct a simulation study using our free software.
