Number Needed to Treat
Chart overview
Number Needed to Treat plots translate absolute risk reduction into clinically intuitive NNT and NNH values, showing how many patients must be treated to achieve one additional benefit or cause one additional harm.
Key points
- Evidence-based medicine practitioners and clinical pharmacologists use NNT visualizations to communicate treatment effect magnitude in a patient-centered way, especially when comparing interventions across multiple outcomes in systematic reviews.
Python Tutorial
How to create a number needed to treat in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Complete Guide to Scientific Data VisualizationExample Visualization
Create This Chart Now
Generate publication-ready number needed to treats with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a number needed to treat (NNT/NNH) plot from my data. Display each outcome as a horizontal bar centered at the NNT or NNH value with confidence interval whiskers. Use a diverging layout with NNT benefit values plotted in one direction and NNH harm values in the opposite direction. Label each outcome on the y-axis and add a vertical reference line at the origin. Use journal formatting with Arial font and no top or right spines."
How to create this chart in 30 seconds
Upload Data
Drag & drop your Excel or CSV file. Plotivy securely processes it in your browser.
AI Generation
Our AI analyzes your data and generates the Number Needed to Treat code automatically.
Customize & Export
Tweak the design with natural language, then export as high-res PNG, SVG or PDF.
Newsletter
Get one weekly tip for better number needed to treats
Join researchers receiving concise Python plotting techniques to improve chart clarity and reduce revision cycles.
Python Code Example
Console Output
Figure saved: plotivy-number-needed-to-treat.png
Common Use Cases
- 1Summarizing absolute treatment effects across multiple outcomes for clinical guidelines panels
- 2Comparing NNT values across competing pharmacological interventions in formulary decisions
- 3Communicating treatment benefits and harms simultaneously for patient decision aids
- 4Presenting subgroup NNT analyses in randomized controlled trial publications
Pro Tips
Use a log scale for the NNT axis when values span a wide range to maintain visual proportion
Distinguish NNT from NNH with contrasting colors such as green and red consistently
Annotate any infinite NNT (no significant absolute risk reduction) with a special symbol
Include absolute risk difference and event rates in a supplementary table linked to each NNT estimate
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 number-needed-to-treat.
numpy
Useful in specialized workflows that complement core Python plotting libraries for number-needed-to-treat analysis tasks.
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.