SYSTEMATIC REVIEWS AND META-ANALYSIS

How to Interpret a Funnel Plot

Guide · 1 min read · Last updated 2026-10-31

A funnel plot graphs each study's effect estimate against a measure of its precision (often standard error). In the absence of bias, small imprecise studies scatter widely at the base, large precise ones cluster near the top, forming a symmetrical inverted funnel around the pooled effect.

How to read it

  1. X-axis: effect size (for example log odds ratio). Y-axis: precision (standard error, inverted so that large studies are at the top).
  2. Vertical line: the pooled estimate.
  3. Look at symmetry. Gaps in one lower corner, often where small studies with null or negative results would sit, suggest asymmetry.

What asymmetry may mean

Asymmetry suggests small-study effects. Possible causes include publication bias, selective outcome reporting, poor methodology in small studies, true heterogeneity (for example a stronger effect in high-risk groups that are studied in small trials), artefact due to the choice of effect measure, and chance.

Cautions

  • Interpretation is subjective; use tests (for example Egger's regression test) as an aid, not a verdict.
  • Tests have low power with few studies. A common recommendation is to examine funnel plots only when there are at least 10 studies.
  • Funnel plots of ratio measures should be drawn on a log scale.

Illustrative example

Invented for teaching. A plot of 14 trials shows large studies near the pooled effect and small studies scattered mainly to the left (favouring the intervention), with an empty area on the right. The authors report possible small-study effects, run a sensitivity analysis excluding small trials, and downgrade certainty for possible publication bias.

Common pitfalls

  • Calling asymmetry "proof" of publication bias.
  • Drawing plots with fewer than 10 studies.

Key takeaways

  • A funnel plot is a visual check of small-study effects.
  • Consider several explanations and use caution.

References and further reading

  1. Sterne JAC, Sutton AJ, Ioannidis JPA, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002. doi:10.1136/bmj.d4002
  2. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629-634. doi:10.1136/bmj.315.7109.629
  3. Higgins JPT, Thomas J, Chandler J, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. Cochrane. Link

Related reading

This page is general educational information about research methods. It is not medical, legal or regulatory advice; follow your institution's ethics board and your target journal's instructions.