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Sign in to get your referral linkSurvival analysis is the cornerstone of clinical trial reporting. Plotivy generates Kaplan-Meier curves with censoring marks, median survival annotations, log-rank p-values, and number-at-risk tables from your data.
The Kaplan-Meier estimator is a non-parametric method for estimating the survival function from time-to-event data. It handles censored observations (patients lost to follow-up) and is the standard reporting format required by JAMA, NEJM, and Lancet.
Drops at each event time - the characteristic staircase shape
Vertical marks showing when patients left the study without events
Time at which 50% survival probability is reached
Compares curves between groups - the primary statistical test
This code implements the full KM estimator from scratch, draws step curves for two treatment arms, adds censoring marks, median survival annotations, and a log-rank p-value. Everything is editable.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Forest plots display hazard ratios from Cox regression - the standard companion to KM curves in clinical publications. The reference line at HR=1 separates protective and harmful effects.
Preparing preview
Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Upload time-to-event data and Plotivy computes the KM estimator, handles censoring, and draws step curves automatically.
Statistical comparison of survival curves with p-value annotation - no manual calculation required.
Generates the risk table below the curve that most clinical journals require for publication.
Figures meet CONSORT reporting guidelines with proper censoring marks, labels, and sizing.
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More visualization types for clinical research
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Displays data distribution using quartiles, median, and outliers in a standardized format.
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 gene expression data for 4 genotypes
np.random.seed(42)
genotypes = ['WT', 'KO1', 'KO2', 'Mutant']
n_per_group = 20
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)
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
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 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 stylingUpload time-to-event data and get journal-ready Kaplan-Meier curves with log-rank tests and number-at-risk tables in seconds.