More than words: Novel visualizations for evidence synthesis

Scientists from all fields very often have to choose from a plethora of data visualization tools for presenting a theory, the results of an experiment or the conclusions of their research. Graphs are a popular means for doing so. Several attributes of graphs have been recommended as indicatives of their power; these include their clarity, precision and efficiency and their ability to show patterns and structures better than other means. Even simple, one-dimensional graphs, often have the ability to reveal patterns, which would not be evident if the information was organized differently. Such an example is a graph presented by Michael Florent van Langren, a Flemish astronomer, to the court of Spain in 1664 (Figure 1). The graph, believed to be the first visual representation of statistical data, represents 12 estimates of the difference in longitude between Toledo and Rome. The graph shows with impressive succinctness the 12 estimates simultaneously, ordered according to their values, their variability, the names of the astronomer who provided each observation, the zero point, and the center of the range of the values, which van Langren indicated by writing “ROMA” on it. In recent years, it would not be easy to find a meta-analysis not accompanied by a forest plot. Their undoubtful popularity is a product of their power, in conjunction with their simplicity. In fact, forest plots are in essence a version of Figure 1, having of course some noticeable differentiations: vertical instead of horizontal display, inclusion of 95% confidence intervals, inclusion of squares proportional to study precision, display of relative instead of absolute estimates, inclusion of the line of no effect and, in place of van Langren's “ROMA,” a diamond lying at the bottom of the plot representing the meta-analytic summary effect. With forest plot being the most popular onedimensional meta-analytic graph, funnel plot is the most popular two-dimensional one. As a scatter plot showing effect sizes against a measure of studies' precision, a funnel plot gives insight in the assessment of small-study effects. The explosion of developments in meta-analytic methodology over the last decades has been accompanied by a respective explosion of visualization methods for presenting those developments. Thus, apart from the forest plot and the funnel plot, a large array of visualization methods has been proposed in the literature since 1978. The present special issue is a collection of 13 articles suggesting new visualization tools or modifications of existing tools for efficiently presenting complex data structures, methodologies or outputs produced by evidence synthesis. The issue covers a range of scientific fields with interest in evidence synthesis, such as medicine, ecology, economic analysis, and agricultural research, while some of the proposed tools can be used across several fields. Special attention has been given to visualizations that can serve as alternative to the forest plot. Specifically, Nakagawa et al. introduce the “orchard plot,” a modified forest plot tailored to the needs of meta-analyses of ecological studies, Rücker et al. propose the drapery plot that depicts the p-value functions, Papakonstantinou et al. suggest a representation of meta-analysis using springs primarily for teaching purposes, while Hilton Boon et al. use a forest-like graph to present a qualitative synthesis approach. The issue continues with the article by Cooper et al. on the so-called enhanced Summary Receiver Operating Characteristic curve for meta-analyses of diagnostic test accuracy (DTA) studies. Then, different options are provided in terms of software and automation using the