A funnel plot is a graphical tool used in meta-analysis to detect publication bias and small-study effects. It is a scatter plot of the effect sizes from individual studies against a measure of study precision (typically the standard error or sample size).

Structure of a Funnel Plot:

• X-axis: Effect size (e.g., odds ratio, mean difference).
• Y-axis: Precision (often standard error or inverse variance).
• Shape: A symmetrical, inverted funnel under ideal conditions.

Use in Meta-Analysis:

1. Detecting Publication Bias: If smaller studies with negative or non-significant results are missing, the plot appears asymmetrical.
2. Assessing Small-Study Effects: Studies with small sample sizes tend to show more variable effect sizes.
3. Evaluating Heterogeneity: A symmetrical plot suggests homogeneity, whereas an asymmetrical one suggests heterogeneity or bias.

Interpreting a Funnel Plot:

• Symmetrical plot: Indicates no significant publication bias.
• Asymmetrical plot: Suggests potential bias or small-study effects.
• Egger’s Test or the Trim-and-Fill Method: Statistical methods can further assess asymmetry and adjust for bias.

figure shows a funnel plot with connecting lines forming the 95% confidence funnel:

• Blue dotted lines represent the 95% confidence limits, helping to visualize expected variability.
• The red dashed line shows the mean effect size.
• Studies are plotted as scatter points, and their spread helps assess publication bias.

A symmetrical funnel suggests low bias, while asymmetry may indicate small-study effects or publication bias.