Forest plots are the fastest way to compare study-level effects and confidence intervals in a single figure. Plotivy turns pooled results, subgroup analyses, and clinical evidence summaries into publication-ready Python output with clear reference lines and consistent weighting.
Use a forest plot when you need to compare multiple study estimates against the same clinical question. It is the standard visual format for meta-analysis, odds ratios, hazard ratios, and subgroup effects because it keeps the point estimate, uncertainty interval, and reference threshold in one line.
Combine study-level results without hiding the uncertainty interval.
Show hazard ratios, odds ratios, and treatment effects side by side.
Compare age bands, biomarkers, or treatment strata in one figure.
Use the reference line at 1.0 to separate benefit from risk.
Each study is ordered, labeled, and plotted with a consistent confidence interval scale.
Point estimates remain legible even when the confidence intervals vary widely between studies.
Bias toward clarity with a neutral reference line and balanced spacing for manuscript figures.
Every plot is generated as editable code so reviewers and co-authors can trace the exact settings.
This example plots six studies with weighted square markers, confidence intervals, and a vertical reference line at 1.0. Adjust the effect sizes or interval widths to match your own clinical evidence set.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Other chart templates that support clinical and evidence-synthesis workflows
Displays values for two variables as points on a Cartesian coordinate system.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import pandas as pd
# Generate sample data
np.random.seed(42)
n_samples = 200
height = np.random.normal(170, 8, n_samples)
weight = height * 0.6 + np.random.normal(0, 8, n_samples) - 50
Displays data points connected by straight line segments to show trends over time.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
# Generate temperature data for 3 major US cities over 12 months
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
nyc = [30, 32, 40, 52, 65, 75, 82, 81, 74, 63, 50, 38]
miami = [65, 66, 70, 76, 82, 87, 90, 90, 87, 80, 72, 66]
chicago = [25, 27, 35, 48, 62, 72, 80, 79, 71, 60, 45, 32]
# Create figure with enhanced styling
Compares categorical data using rectangular bars with heights proportional to values.
Sample code / prompt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
# Generate performance scores for 5 treatment groups
np.random.seed(42)
groups = ['Control', 'Treatment A', 'Treatment B', 'Treatment C', 'Treatment D']
n_samples = 30
Displays the distribution of numerical data by grouping values into bins.
Sample code / prompt
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde, skewnorm
# Generate age data with slight right skew
np.random.seed(42)
ages = skewnorm.rvs(a=2, loc=42, scale=15, size=500)
ages = np.clip(ages, 18, 80) # Clip to realistic range
fig, ax = plt.subplots(figsize=(12, 7))
Combines box plots with kernel density to show distribution shape across groups.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway
# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 50)Build a forest plot from your own effect sizes, keep the uncertainty visible, and export a figure that is ready for a paper, slide deck, or clinical report.