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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Bürkner PC,Doebler P

doi

10.1002/sim.6177

subject

Has Abstract

pub_date

2014-08-15 00:00:00

pages

3061-77

issue

18

eissn

0277-6715

issn

1097-0258

journal_volume

33

pub_type

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