How do you use software tools like R, Stata, or RevMan to create and analyze a funnel plot?

1 How to create a funnel plot

There are different software tools that can help you create a funnel plot for your meta-analysis, such as R, Stata, or RevMan. Each tool has its own commands and options, but the general steps are similar. You need to have your data ready in a format that includes the effect size and the standard error (or the sample size) for each study. Then, you need to specify the type of effect size (such as mean difference, odds ratio, or risk ratio) and the summary effect (such as the fixed-effect or the random-effects estimate). Finally, you need to run the command or the function that generates the funnel plot and adjust the appearance and the labels as needed.

2 How to analyze a funnel plot

Once you have created a funnel plot, you need to inspect it visually for signs of asymmetry or deviation from the expected shape. You can also use statistical tests or methods such as Egger's test, Begg's test, and the trim-and-fill method to quantify the degree of asymmetry and determine if there is publication bias. The Egger's test regresses the effect size against the precision and tests whether the intercept is significantly different from zero, while the Begg's test ranks the effect size and precision and tests for correlation using Kendall's tau. The trim-and-fill method imputes the missing or unpublished studies based on the symmetry of the funnel plot and recalculates the summary effect; a large difference between the original and adjusted summary effect indicates publication bias.

3 How to interpret a funnel plot

When interpreting a funnel plot, it’s important to note that it is not a definitive test for publication bias, but instead a visual aid that can suggest its presence or absence. Additionally, asymmetry can be caused by other factors such as methodological quality, clinical diversity, or data errors. Furthermore, the choice of effect size and precision measure can influence the shape and results of a funnel plot. It’s also important to consider the number and size of studies included in the meta-analysis as a small number of studies may make the funnel plot unreliable or difficult to interpret and large variations in study size may create an impression of asymmetry even when there is none.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?