Department of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010, Vienna, Austria.
BMC Med Res Methodol. 2020 Feb 7;20(1):26. doi: 10.1186/s12874-020-0911-9.
Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.
We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.
We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.
The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.
数据可视化方法对于探索和交流荟萃分析数据和结果至关重要。最近提出了大量新的图形,因此,目前可用的荟萃分析图形选项缺乏全面、最新的概述。
我们应用了多层次搜索策略来查找迄今为止提出和介绍的荟萃分析图形。我们仔细检查了 150 多本可检索的研究综合方法教科书,涵盖了六种常用于荟萃分析的不同软件程序,以及两个领先的研究综合期刊的全部内容。此外,我们还进行了 Google Scholar 和 Google 图像搜索,并对该主题的先前评论进行了参考文献搜索。检索到的图形被分类为一个包含 11 个主要类别的分类法,根据 24 个图形功能特征进行评估,并通过解释性示例单独呈现和描述。
我们确定了 200 多种不同的图形和图形变体,用于可视化荟萃分析数据。其中一半仅在过去 10 年中积累。最常见的类别是网络荟萃分析图形(45 个显示)、仅显示综合效果的图形(26 个)、漏斗图类似的显示(24 个)、显示每个研究多个结果的显示(19 个)、稳健性、异常值和影响诊断(15 个)、研究选择和 p 值为基础的显示(15 个)和森林图类似的显示(14 个)。大多数图形(130 个,占 62.5%)具有独特的图形特征组合。
可用的荟萃分析图形种类繁多,提供了多种显示荟萃分析多个方面的选项。这种对可用图形的全面概述使研究人员能够更好地了解哪种图形适合他们的需求,从而促进充分利用荟萃分析图形工具包。它还为有针对性地开发进一步的研究综合图形显示提供了路线图。