Editorial commentary
A forest plot is the graphical display used in a meta-analysis to show individual study effect estimates alongside the pooled (combined) summary estimate. It is the standard figure format specified or exemplified in the PRISMA 2020 reporting guideline and in the Cochrane Handbook’s chapter on meta-analysis, and it is normally what a reader looks to first when assessing a meta-analysis‘s results.
What makes a chart a forest plot
A forest plot has a consistent structure regardless of the software used to generate it (RevMan, R’s meta/metafor packages, Stata, and similar tools all produce the same layout):
- One row per included study. Each row shows the study’s point estimate as a square (or sometimes a diamond or circle) positioned on the effect-size axis, with a horizontal line extending through it marking the study’s confidence interval — conventionally 95%.
- Square size reflects study weight. Larger squares indicate studies that contribute more weight to the pooled estimate, typically because they have larger sample sizes or narrower confidence intervals (greater precision).
- A vertical reference line at the null value. This is placed at 0 for difference-based effect measures (mean difference, risk difference) or at 1 for ratio-based measures (odds ratio, risk ratio, hazard ratio). If a study’s confidence-interval line crosses this reference line, that individual study’s result is not statistically significant on its own.
- A summary diamond at the bottom. This represents the pooled effect estimate produced by combining all included studies statistically. The diamond’s horizontal center marks the pooled point estimate; its left and right tips mark the pooled estimate’s confidence interval. If the diamond does not cross the null-value reference line, the pooled result is statistically significant.
- A numeric summary column alongside the plot, typically listing each study’s effect estimate, confidence interval, and percentage weight, plus the pooled statistics and a heterogeneity statistic (commonly I²).
Why forest plots matter
A forest plot lets a reader assess two things at a glance that a table of numbers does not make as visible: whether the pooled effect is being driven consistently across studies or by one or two outliers, and how much the individual study estimates disagree with each other (heterogeneity). Widely scattered point estimates with little overlap between confidence intervals is a visual signal of heterogeneity, which is normally also reported as a formal statistic (I²) alongside the plot. Per the Cochrane Handbook, I² interpretation bands deliberately overlap and should not be read as a mechanical cutoff — the plot itself, showing where the disagreement actually sits, is part of how a reader is meant to judge it rather than relying on the summary statistic alone.
Forest plots also make it possible to see immediately whether a particular subgroup or a particular study is pulling the pooled estimate in one direction, which is why sensitivity analyses and subgroup analyses in a meta-analysis are usually presented as their own forest plots.
Worked example
Consider a meta-analysis of eight randomized controlled trials comparing a treatment to placebo, pooled using a random-effects model, reporting odds ratios (OR):
- Each of the eight trials appears as one row, with a square at its OR and a horizontal line spanning its 95% confidence interval.
- The vertical reference (null) line sits at OR = 1.0, since an odds ratio of 1 means no difference between treatment and placebo.
- Trials with wide confidence intervals that cross OR = 1.0 are not statistically significant individually; a trial whose entire confidence interval sits to the left of 1.0 favors treatment on its own.
- The diamond at the bottom shows the pooled OR (for example, 0.78) with its own 95% confidence interval (for example, 0.68–0.89). Because the diamond does not cross OR = 1.0, the pooled result is statistically significant in favor of treatment, even though not every individual trial was.
- An I² statistic reported alongside (for example, I² = 45%) indicates the degree of heterogeneity among the eight trials’ individual estimates.
What is not a forest plot
A generic scatter plot or bar chart of effect sizes across studies, without confidence-interval lines and a pooled summary diamond, is not a forest plot — it is missing the specific elements (per-study CIs, weighting reflected in marker size, and a combined pooled estimate) that define the format. A funnel plot is a related but distinct chart used in meta-analysis: it plots each study’s effect estimate against a measure of precision (such as standard error) to visually assess asymmetry associated with small-study effects and possible publication bias, rather than displaying individual confidence intervals row by row or a pooled diamond.
How to read a forest plot step by step
To read a forest plot, find the vertical line of no effect first, then judge each study’s confidence-interval line against it, check which studies carry the most weight, and finish with the pooled diamond and the heterogeneity statistic. For a longer walkthrough with annotated figures, see the guide on how to read a forest plot.
- Check the axis and the labels. Confirm which effect measure is plotted (odds ratio, risk ratio, hazard ratio, mean difference) and which side favours the intervention. Ratio measures such as the odds ratio have a null value of 1; differences have a null value of 0.
- Find the line of no effect. A study whose confidence interval crosses it is compatible with no difference.
- Look at each study’s interval. Wide lines mean imprecise estimates; narrow lines mean precise ones.
- Check the weights. In the Cochrane Handbook’s description, the area of each block indicates the weight assigned to that study in the meta-analysis (Cochrane Handbook, Chapter 10).
- Read the diamond. Its centre is the pooled estimate and its width is the pooled confidence interval.
- Finish with heterogeneity. Look at whether study estimates overlap, then at I² and its confidence interval.
How to interpret a forest plot: fixed-effect vs random-effects
The model used to compute the diamond changes what it means. The Cochrane Handbook explains that a fixed-effect meta-analysis assumes the true effect is the same value in every study, so differences between studies are attributed to chance. A random-effects meta-analysis instead assumes the effects follow a distribution, usually normal, so the summary estimate describes an average effect across studies and its confidence interval reflects both chance and genuine variation. The plot’s footer normally states which model was used, so check it before interpreting the diamond.
| Question | Fixed-effect | Random-effects |
|---|---|---|
| Assumption | One true effect shared by all studies | True effects vary across studies |
| What the diamond estimates | The common effect | The average of the distribution of effects |
| Differences between studies attributed to | Chance | Chance plus real variation |
Source: Cochrane Handbook, Chapter 10.
Forest plot heterogeneity and I²
The Handbook gives rough bands for interpreting I² in meta-analyses of randomized trials: 0% to 40% might not be important, 30% to 60% may represent moderate heterogeneity, 50% to 90% may represent substantial heterogeneity, and 75% to 100% considerable heterogeneity. The bands overlap on purpose, and the Handbook warns that thresholds for interpreting I² can be misleading, because importance depends on the size and direction of effects and the strength of evidence for heterogeneity (Cochrane Handbook, Chapter 10). A random-effects confidence interval does not describe the degree of heterogeneity, so a prediction interval is the better way to show the spread of underlying effects. For causes and remedies, see heterogeneity in meta-analysis.
Making a forest plot
Forest plots are generated by the meta-analysis software itself rather than drawn by hand, so the layout is consistent across tools. For R users, the walkthrough on meta-analysis in R with metafor shows how the pooled model and its plot are produced.
Related CASRAI content
- Meta-analysis — the statistical procedure a forest plot visualizes.
- Systematic review — the broader review process a meta-analysis (and its forest plot) is often embedded within.
- Systematic Review vs. Meta-Analysis — comparison page.
- PRISMA and Systematic Review Methodology — guide.
- PRISMA 2020 — the reporting guideline that specifies systematic-review and meta-analysis reporting, including result figures such as forest plots.
References
- Cochrane Handbook for Systematic Reviews of Interventions, Chapter 10 (Analysing data and undertaking meta-analyses) — training.cochrane.org/handbook
- Page MJ, McKenzie JE, Bossuyt PM, et al. “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.” BMJ. 2021;372:n71.
Frequently Asked Questions
How do you read a forest plot?
Each row represents one included study, shown as a square positioned at that study’s effect estimate with a horizontal line marking its confidence interval (conventionally 95%); square size reflects how much weight that study carries in the pooled analysis. A vertical reference line marks the null value — 0 for difference-based measures or 1 for ratio-based measures — and a diamond at the bottom shows the pooled summary estimate across all included studies.
What does the diamond at the bottom of a forest plot represent?
The diamond represents the pooled effect estimate produced by statistically combining all the included studies. Its horizontal center marks the pooled point estimate, and its left and right tips mark the pooled estimate’s confidence interval; if the diamond does not cross the null-value reference line, the pooled result is statistically significant.
What does it mean when a study’s confidence-interval line crosses the vertical reference line?
It means that individual study’s result is not statistically significant on its own. The vertical reference line marks the null value: 0 for difference-based effect measures such as mean difference or risk difference, and 1 for ratio-based measures such as odds ratio, risk ratio, or hazard ratio.
What software is used to create forest plots?
Forest plots are commonly produced with RevMan, R’s meta/metafor packages, and Stata, among other tools. Regardless of the software used, the layout is standard: one row per study with a point estimate and confidence-interval line, plus a pooled summary diamond.
Is a forest plot the same as a funnel plot?
No. A forest plot shows each study’s effect estimate and confidence interval alongside a pooled summary diamond, while a funnel plot plots each study’s effect estimate against a measure of precision (such as standard error) to visually assess asymmetry associated with small-study effects and possible publication bias.
Examples
Worked examples
- Is an instance
A meta-analysis of eight RCTs comparing a treatment to placebo, reporting each trial's odds ratio and 95% CI as a square-and-line row, with a summary diamond at pooled OR 0.78 (95% CI 0.68-0.89) that does not cross the null line at OR = 1.0
- Is an instance
A forest plot generated by RevMan or R's metafor package showing per-study weights (square size) alongside an I-squared heterogeneity statistic for the included studies
Counter-examples
Looks similar, but isn't
- Not an instance
A bar chart of effect sizes across studies with no confidence-interval lines and no pooled summary diamond is not a forest plot
- Not an instance
A funnel plot (effect estimate vs. precision, used to assess small-study effects/publication bias) is a related but structurally distinct meta-analysis chart, not a forest plot
Also known as
Blobbogram
Machine-readable encodings
Use in your systems
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