From data table to figure in a minute
Upload a CSV or Excel file, or start from the built-in example. Pick your effect measure: odds ratio, risk ratio, hazard ratio, mean difference, or standardized mean difference. Choose a fixed-effect or random-effects model. The forest plot renders immediately, with the pooled estimate, weights, confidence intervals, and heterogeneity statistics underneath.
Two renderers, one for the journal and one for you
The default renderer produces a clean publication SVG, the kind of figure you can drop into a manuscript without touching it. A second, interactive renderer (built on Plotly) lets you hover, zoom, and inspect each study. Both export to SVG or PNG.
The statistics behind the plot
- Inverse-variance pooling, fixed and random effects
- DerSimonian-Laird and REML estimators for between-study variance
- Cochran's Q, I², and tau² reported with every analysis
- Funnel plot with Egger's and Begg's tests for small-study effects
- Subgroup analysis, meta-regression, and leave-one-out sensitivity analysis
The engine is validated against R's metafor package, and every method shows its formula and citation. If your journal or supervisor wants the analysis reproduced in R, you can download replication code from the Methods page.
When you need more than one plot
A forest plot usually belongs to a larger review. Covexe runs the whole pipeline in the browser: screening, data extraction, risk of bias, GRADE, and the PRISMA 2020 flow diagram, all in the same free project.