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Dr. Sedgwick says that funnel plot is the plot between treatment effect (x-axis) and inverse of standard error (y-axis). Here we like to add that , not only inverse of standard error, it can be a measure of study size, e.g. sample size and inverse of variance (weight) of treatment effect. Sterne and Egger have compared the above and found inverse of standard error as more appropriate.1 Asymmetry of the plot tells the reporting bias and methodological anomalies
1. Jonathan A. C. Sterne & Matthias Egger (October 2001). "Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis". Journal of Clinical Epidemiology 54 (10): 1046–1045. doi:10.1016/S0895-4356(01)00377-8