Background and objective: Publication bias and other sample size effects are issues for meta-analyses of test accuracy, as for randomized trials. We investigate limitations of standard funnel plots and tests when applied to meta-analyses of test accuracy and look for improved methods.
Methods: Type I and type II error rates for existing and alternative tests of sample size effects were estimated and compared in simulated meta-analyses of test accuracy.
Results: Type I error rates for the Begg, Egger, and Macaskill tests are inflated for typical diagnostic odds ratios (DOR), when disease prevalence differs from 50% and when thresholds favor sensitivity over specificity or vice versa. Regression and correlation tests based on functions of effective sample size are valid, if occasionally conservative, tests for sample size effects. Empirical evidence suggests that they have adequate power to be useful tests. When DORs are heterogeneous, however, all tests of funnel plot asymmetry have low power.
Conclusion: Existing tests that use standard errors of odds ratios are likely to be seriously misleading if applied to meta-analyses of test accuracy. The effective sample size funnel plot and associated regression test of asymmetry should be used to detect publication bias and other sample size related effects.