Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials¶
Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., et al. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Funnel Plot Asymmetry
- Statistical formalizations — Egger's regression of the standardized effect on precision, Begg's rank correlation, the trim-and-fill estimator of the missing studies — operationalize the visual pattern as a testable quantity.
This sourceReviews the statistics that formalise funnel plot asymmetry — Egger's regression of standardised effect on precision, Begg's rank correlation test, and trim and fill among the adjustment methods — and sets out in Box 1 the rival causes (reporting bias, poor methodological quality, true heterogeneity, artefact, chance) an asymmetric plot leaves open; the paper's own emphasis falls on regression-based tests, with the rank-correlation test and trim and fill treated as secondary.
SupportedVerified against the source
- Statistical formalizations — Egger's regression of the standardized effect on precision, Begg's rank correlation, the trim-and-fill estimator of the missing studies — operationalize the visual pattern as a testable quantity.
- Publication Bias
- Genuine heterogeneity, quality differences, analytic choices, and chance can create the same observable
This sourceThe consensus statement that funnel plot asymmetry must not be equated with publication bias, enumerating true heterogeneity, methodological quality, effect-measure and model choices, and chance among its other causes.
SupportedVerified against the source
- Genuine heterogeneity, quality differences, analytic choices, and chance can create the same observable
Verification¶
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