forest-plot-creation

To create a forest plot in R, you need a dataset containing study effect sizes (yi) and standard errors (sei) to fit a random-effects model using the metafor package. This Skill guides you through generating the plot and customizing it with subgroup labels.

You need a dataset containing yi (effect sizes) and sei (standard errors) values to generate a forest plot with the metafor package. Providing these inputs allows the Skill to visualize study effects, weights, and confidence intervals across your meta-analysis.

Yes, you can customize forest plots to display subgroups, subgroup labels, and model-type notes for transparent reporting. This ensures your visualization clearly communicates heterogeneity and random-effects model details for manuscript figures.

To interpret a forest plot, this Skill explains how to read the plot, identify the line of no effect, and visually assess heterogeneity across studies. It helps you understand individual study weights and confidence intervals at a glance.

Yes, you need R with the metafor package installed to produce and customize forest plots. This Skill requires your input data, a fitted random-effects model, and this environment to visualize your statistical analysis results effectively.