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The biggest threats to meta-analysis are publication bias due to missing studies with negative results and low-quality evidence due to methodological limitations imposed by included studies.
More specifically, Duval and Tweedie's trim and fill suggested that there may be two missing studies with a negative effect (impaired emotion recognition in anxiety disorders compared to controls).
Funnel plots of all studies reporting unadjusted mortality indicate a potential for missing studies with inverse associations (RR <1.0).
The identification of 11 possible missing studies with correlations greater than the pooled mean may simply be a reflection of heterogeneity in the data.
Funnel plots of all studies reporting unadjusted and adjusted mortality with IAT indicated no potential for missing studies with inverse associations (RR <1.0).
The funnel plot suggests a couple of missing studies with OR higher than 1, but the small number of included trials does not allow drawing conclusions about publication bias.
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This suggests that we most likely did not miss studies with a negative outcome.
We found that the specific search tended to miss studies with general or multiple health outcomes, while the generic search tended to miss studies with single specific health outcomes.
By restricting the search to studies that focused on breast cancer specifically, we may have missed studies with a more general title of 'cancer' during title review.
If publication bias was suspected, results are shown without imputation and with "missing" studies imputed with Duval and Tweedie's trim and fill method.
Peter Lee [ 17] drew attention in Statistics in Medicine (SiM) to the problem with the BMJ paper on passive smoking but a recent paper [ 38] in SiM not only does not cite Lee but cites the paper on passive smoking and uses it to illustrate a method to deal with missing studies, the opposite of the known problem!
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Justyna Jupowicz-Kozak
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