Funnel plot

Funnel plots, introduced by Light and Pillemer in 1984[1] and discussed in detail by Matthias Egger and colleagues,[2][3] are useful adjuncts to meta-analyses.

Whatever the cause, an asymmetric funnel plot leads to doubts over the appropriateness of a simple meta-analysis and suggests that there needs to be investigation of possible causes.

[3] When the standard error is used, straight lines may be drawn to define a region within which 95% of points might lie in the absence of both heterogeneity and publication bias.

Since funnel plots are principally visual aids for detecting asymmetry along the treatment effect axis, this makes them considerably easier to interpret.

[4] The appearance of the funnel plot can change quite dramatically depending on the scale on the y-axis — whether it is the inverse square error or the trial size.

An example funnel plot showing no publication bias. Each dot represents a study (e.g. measuring the effect of a certain drug); the y -axis represents study precision (e.g. the inverse standard error or number of experimental subjects) and the x -axis shows the study's result (e.g. the drug's measured average effect).