The package metaviz is a collection of functions to create visually
appealing and information-rich plots of meta-analytic data using ggplot2.
Functions to create several variants of forest plots (
funnel plots (
viz_sunset), and to conduct visual
inference with funnel plots (
funnelinf) are provided.
Several different types and variants of forest plots can be created. This includes classic forest plots, subgroup forest plots, cumulative summary forest plots, and leave-one-out sensitivity forest plots. In addition, the function allows to individually label and color studies and to align tables with furhter study-level and summary-level information.
In addition to traditional forest plots, rainforest plots as well as thick forest plots can be used.
Rainforest and thick forest plots are two recently proposed variants and enhancements of
the classic forest plot. Both variants visually emphasize large studies
(with short confidence intervals and more weight in the meta-analysis), while small studies
(with wide confidence intervals and less weight in the meta-analysis) are visually less dominant.
For further details see
Numerous different funnel plot variants can be created. Options for several graphical augmentations
(e.g., confidence, significance, and additional evidence contours; choice of the ordinate; showing
study subgroups), and different statistical information displayed are provided (Egger's regression line,
and imputed studies by, as well as the adjusted summary effect from, the trim-and-fill method).
Further details and references can be found in the corresponding help file (
Moreover, a novel variant of the funnel plot is introduced which displays the power of studies to detect an effect
of interest (e.g., the meta-analytic summary effect) using a two-sided Wald test. This sunset (power-enhanced)
funnel plot uses color-coded regions and a second y axis to visualize study-level power and can help to
critically examine the evidentiality and credibility of a set of studies. For further details see
Funnel plots are widely used in meta-analysis to assess small study effects as potential indicator of publication bias.
Visual inference can help to improve the objectivity and validity of conclusions based on funnel plot examinations by
guarding the meta-analyst from interpreting patterns in the funnel plot that might be perfectly plausible by chance.
Only if the funnel plot showing the real data is distinguishable from simultaneously presented
null funnel plots showing data simulated under the null hypothesis, conclusion based on visually inspecting
the real-data funnel plot might be warranted. The function
funnelinf provides numerous tailored
options to conduct visual inference with the funnel plot graph in the context of meta-analysis. See
help(funnelinf) for further details and relevant references.
Four different example datasets from published meta-analyses are distributed with the package:
Two datasets for meta-analysis with standardized mean differences (
One dataset for meta-analysis with correlation coefficients (
One dataset for meta-analysis with dichotomous outcome data (
More details and corresponding references can be found in the respective help files
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.