Bias in meta-analysis detected by a simple, graphical testBMJ 1997; 315 doi: https://doi.org/10.1136/bmj.315.7109.629 (Published 13 September 1997) Cite this as: BMJ 1997;315:629
- Matthias Egger, reader in social medicine and epidemiology ()a,
- George Davey Smith, professor of clinical epidemiologya,
- Martin Schneider, research associateb,
- Christoph Minder, head, medical statistics unitb
- a Department of Social Medicine, University of Bristol, Bristol BS8 2PR
- b Department of Social and Preventive Medicine, University of Berne, CH-3012 Berne, Switzerland
- Correspondence to: Dr Egger
- Accepted 26 August 1997
Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses.
Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews.
Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision.
Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias.
Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials
Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases
Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials
Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews
Critical examination of systematic reviews for publication and related biases should be considered a routine procedure