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Fig. 3 Deeks' funnel plot asymmetry test for publication bias Fig. 4 Forest plots of the sensitivity and specificity for presepsin across all included studies Fig. 5 a Hierarchical summary receiver operating characteristic plot of presepsin across all included studies.
Two methods to test for publication bias, a common problem in meta-analysis, suggested that whilst the presence of unpublished small-size studies is probable, it is not likely that this would significantly bias the overall chilling effect estimated in this study.
The popular funnel-plot-based methods to test for publication bias in meta-analysis are inappropriate and potentially misleading when the number of included studies is small and heterogeneity is large [25], [26].
Funnel plots were generated to test for publication bias.
We used Egger's regression [ 50] to test for publication bias.
metabias performs the Begg and Mazumdar (1994)) adjusted rank correlation test for publication bias as well as the Egger et al (1997) regression asymmetry test for publication bias.
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This section reports tests for publication bias using two techniques.
We also tested for publication bias among our sample of studies using two approaches.
We tested for publication bias using funnel plot analysis.
The second mechanism results from selecting one of the two tests for publication.
Tests for publication bias among all estimates combined were suggestive of an absence of smaller studies for COPD (Figures S7, S8, S9, S10).
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