title: Testing for publication bias in diagnostic meta-analysis: a simulation study.
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 ω), 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 ω 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 ω to detect funnel plot asymmetry in diagnostic meta-analysis.
authors.affiliation: Psychology and Sport Sciences, Fliednerstr. 21, D-48149, Münster, Germany.
authors.forename: Paul-Christian
authors.initials: PC
authors.lastname: Bürkner
authors.forename: Philipp
authors.initials: P
authors.lastname: Doebler
mesh_terms: D056808:Biostatistics; D003198:Computer Simulation; D003933:Diagnosis; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016017:Odds Ratio; D017594:Publication Bias / Q000706:statistics & numerical data*; D012044:Regression Analysis
keywords: diagnostic meta-analysis; diagnostic odds ratio; publication bias; simulation study; trim and fill