  The present study investigates the performance of several statistical tests
to detect publication bias in diagnostic meta-analysis by means of simulation.
While bivariate models should be used to pool data from primary studies in
diagnostic meta-analysis, univariate measures of diagnostic accuracy are
preferable for the purpose of detecting publication bias. In contrast to
earlier research, which focused solely on the diagnostic odds ratio or its
logarithm ($\ln\omega$), the tests are combined with four different univariate
measures of diagnostic accuracy. For each combination of test and univariate
measure, both type I error rate and statistical power are examined under
diverse conditions. The results indicate that tests based on linear regression
or rank correlation cannot be recommended in diagnostic meta-analysis, because
type I error rates are either inflated or power is too low, irrespective of the
applied univariate measure. In contrast, the combination of trim and fill and
$\ln\omega$ has non-inflated or only slightly inflated type I error rates and
medium to high power, even under extreme circumstances (at least when the
number of studies per meta-analysis is large enough). Therefore, we recommend
the application of trim and fill combined with $\ln\omega$ to detect funnel
plot asymmetry in diagnostic meta-analysis. Please cite this paper as published
in Statistics in Medicine (https://doi.org/10.1002/sim.6177).
