Testing for Publication Bias in Diagnostic Meta-Analysis: A Simulation Study

TL;DR

This simulation study evaluates statistical tests for detecting publication bias in diagnostic meta-analysis, finding that trim and fill with the logarithm of diagnostic odds ratio is most reliable.

Contribution

It introduces a comprehensive simulation-based comparison of tests for publication bias, recommending the trim and fill method with $ ext{ln} ext{omega}$ for diagnostic meta-analyses.

Findings

Linear regression and rank correlation tests are unreliable due to inflated error rates.

Trim and fill with $ ext{ln} ext{omega}$ shows good power and controlled error rates.

Recommended method performs well even with many studies.

Abstract

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).