Using meta-analyses, the results of *k* studies related to the same question can be combined to produce an average result. For example, in the context of clinical trials comparing a new pharmaceutical with a placebo, the treatment effect in each trial may be quantified by the odds ratio. Each of the *k* effect estimates is recorded and finally summarized to one average estimate.

There are two different approaches in meta-analysis. The fixed-effects (FE) model assumes that the same treatment effect, *θ*, underlies all studies. Different estimates
for the true effect *θ*, resulting from the *k* studies are expected to arise solely from sampling error. By contrast, the random-effects (RE) model incorporates the between-study variation, taking into account the heterogeneous true effects
[1]. This model is appropriate when the observed treatment effects between studies differ more from each other than would be expected from within-study variation alone. This heterogeneity between studies may arise from diversity in participants or interventions. The FE model can be viewed as a special case of the RE model, in which the between-study variation is 0.

The parameter to be estimated depends on the approach chosen. Under the assumption of a FE one true effect is estimated, whereas under the assumption of RE, the expected value *θ* of the distribution of true effects is estimated. Despite this difference between the two approaches, both the graphical presentation and the interpretation of the results are in practice the same for both models. The point and interval estimates of *θ* are commonly displayed in a forest plot as a diamond, irrespective of the model chosen [2–4]. Commonly used software packages in systematic reviews (for example, RevMan [5] in Cochrane reviews) do not distinguish between the two models in the graphical presentation of results. Apart from a numerical value, the estimate of the between-study variation, *τ*, is not shown in forest plots of RE models.

The use of the prediction interval (PI) has recently been proposed to illustrate the degree of heterogeneity in forests plots of RE meta-analyses [6–8]. A PI provides a predicted range for the true treatment effect in an individual study. Higgins *et al*. [7] proposed using an additional hollow diamond for the presentation of PIs, whereas Riley *et al*. [8] added extra lines to the usual diamond of the effect estimate and its CI. For explanatory purposes, Borenstein *et al*. [9] displayed a bell-shaped curve, truncated at the limits of the PI, in accordance with the assumption of normally distributed effects; however, to display the PI, they adopted the same graphical approach as the one proposed by Riley *et al*. [8].

In this paper, we propose a new graphical approach for the presentation of PIs based on the original suggestion by Skipka [6], and compare it with the approaches of Higgins *et al*. [7] and Riley *et al*. [8].