Bias in Meta-Analysis Detected by a Simple, Graphical Test¶
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in Meta-Analysis Detected by a Simple, Graphical Test. BMJ, 315(7109), 629-634.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Paradox of Unanimity
- In scientific replication and meta-analysis, a literature in which every study confirms an effect is read as evidence of publication bias, p-hacking, or a shared confound, and funnel-plot asymmetry tests exploit exactly this shape.
This sourceFunnel-plot asymmetry exploits too-uniform agreement across studies as a signature of publication bias.
- In scientific replication and meta-analysis, a literature in which every study confirms an effect is read as evidence of publication bias, p-hacking, or a shared confound, and funnel-plot asymmetry tests exploit exactly this shape.
- Selection Bias
This sourceEgger funnel plot meta-analysis publication bias detection.
Domain-specific¶
- Funnel Plot Asymmetry
- The textbook case is intravenous magnesium after acute myocardial infarction, used by Egger et al. (BMJ, 1997) to introduce their regression test.
This sourceIntroduces the regression of standardised effect on precision as a test of funnel plot asymmetry and reports the intravenous-magnesium-after-myocardial-infarction meta-analysis as markedly asymmetric (intercept -1.36, P=0.005) — one of four meta-analysis/large-trial discordant pairs within a survey of 75 meta-analyses, rather than the paper's sole worked example.
- The textbook case is intravenous magnesium after acute myocardial infarction, used by Egger et al. (BMJ, 1997) to introduce their regression test.
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