BMJ 1997;315:629-634 (13 September)
Papers
Bias in meta-analysis detected by a simple, graphical test
Matthias Egger,
reader in social
medicine and epidemiology,a
George Davey Smith,
professor
of
clinical epidemiology,a
Martin Schneider,
research
associate,b
Christoph Minder,
head, medical
statistics unit ba 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 m.egger@bristol.ac.uk
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.
|
Key messages
- 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
|

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