Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias.

中文翻译：

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多种结果的元分析的可视化表示：森林图、漏斗图和毛毛虫图的扩展

元分析数据集可能很大，尤其是在初级研究中报告了多个效应大小时。因此，元分析数据的可视化有助于总结数据和理解初级研究中报告的信息。荟萃分析中的黄金标准数字是森林图和漏斗图。然而，这些图都不能解释初级研究中存在多种效应大小。这份手稿描述了漏斗图、森林图和毛毛虫图的扩展，以使其适应三级荟萃分析。对于森林图，我们建议绘制研究特定效应及其精度，并添加额外的置信区间，以反映个体效应大小的抽样方差。对于毛虫图和漏斗图，我们建议在两个单独的图表中绘制单个效应大小和平均研究效应大小。对于漏斗图，绘制单独的图表可能会提高对发表偏倚和/或选择性结果报告偏倚的检测。