Forest Plot
Chart overview
A forest plot (also called a blobbogram) is the standard display for presenting the results of a systematic review and meta-analysis.
Key points
- Each row represents an individual study, showing the study identifier, sample size, effect estimate (odds ratio, hazard ratio, standardized mean difference, or risk ratio), and its 95% confidence interval as a horizontal line with a central square.
- The size of the square is proportional to the study's weight in the meta-analysis, reflecting sample size or precision.
- The vertical line of no effect (OR = 1 or SMD = 0) serves as the reference.
Python Tutorial
How to create a forest plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample Visualization
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"Create a forest plot for my meta-analysis data with columns: study, n_treatment, n_control, effect_size, ci_lower, ci_upper, weight. Plot each study as a square (sized by weight) with horizontal CI lines. Add a vertical line at null effect. Draw the pooled estimate as a diamond. Report I-squared and heterogeneity p-value below. Sort studies by effect size. Format for Lancet or BMJ publication standards."
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Python Code Example
Console Output
Figure saved: plotivy-forest-plot.png
Common Use Cases
- 1Cochrane systematic reviews pooling randomized controlled trial results for clinical interventions
- 2Epidemiology: summarizing relative risk estimates from cohort and case-control studies
- 3Pharmacology: combining dose-response data across independent experiments for drug efficacy
- 4Diagnostic accuracy meta-analysis: pooling sensitivity and specificity across test evaluation studies
Pro Tips
Scale the square size by inverse-variance weight, not raw sample size, for accurate representation
Report both fixed-effects and random-effects pooled estimates when heterogeneity is substantial (I-squared > 50%)
Use a logarithmic x-axis when displaying ratio measures (OR, HR, RR) so confidence intervals are symmetric
Conduct a subgroup analysis and display separate subtotal diamonds to explore sources of heterogeneity
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for forest-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for forest-plot analysis tasks.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.