A first stage in any review is to look at a simple scatter plot, which can yield a surprisingly comprehensive qualitative view of the data. Even if the review does not show the data in this way you can do it from information on individual trials presented in the review tables.
Trials in which the experimental treatment proves better than the control (EER > CER) will be in the upper left of the plot, between the y axis and the line of equality (Figure 1). If experimental is no better than control then the point will fall on the line of equality (EER = CER), and if control is better than experimental then the point will be in the lower right of the plot, between the x axis and the line of equality (EER < CER).
Figure 1: L'Abbé plot for treatment
Visual inspection gives a quick and easy indication of the level of agreement among trials. Heterogeneity is often assumed to be due to variation in the experimental and control event rates, but that variation is often due to the small size of trials.
L'Abbé plots are becoming widely used, probably because people can understand them. They do have several benefits: the simple visual presentation is easy to assimilate. They make us think about the reasons why there can be such wide variation in (especially) placebo responses, and about other factors in the overall package of care that can contribute to effectiveness. They explain the need for placebo controls if ethical issues about future trials arise. They keep us sceptical about overly good or bad results for an intervention in a single trial where the major influence may be how good or bad was the response with placebo.
Ideally a L'Abbé plot should have the symbols appropriate to the size of the trials. In Figure 2, There is an inset for the symbol size, and the two colours show trazodone used for erectile dysfunction in two different conditions (and with clear clinical heterogeneity, Bandolier 116).