SURVIVAL ANALYSIS

Create Survival Curves in 30 Seconds

Generate Kaplan-Meier plots with risk tables and log-rank tests instantly. Plotivy handles censored data and statistics automatically.

Survival Analysis is Critical but Complex

Censored Data

Handling right-censored data correctly in Excel is nearly impossible. You need specialized statistical software.

Risk Tables

Aligning a "Number at Risk" table below the x-axis in Python/matplotlib is notoriously difficult and fragile.

Statistics

Calculating Log-rank p-values or Hazard Ratios requires running separate tests and manually adding them to the plot.

How Plotivy Creates Survival Curves

1

Upload Data

Upload data with 'Time' and 'Event/Status' columns.

2

Describe Analysis

"Plot Kaplan-Meier curves for 'Treatment'. Show risk table and p-value."

3

Export

Download a publication-ready figure with statistics included.

Example Prompt

“Create a Kaplan-Meier survival plot. Use 'Time_Months' for time and 'Status' (1=dead, 0=censored) for events. Compare 'Drug A' vs 'Placebo'. Add a risk table below the x-axis. Calculate and display the Log-rank test p-value.”

✨ Plotivy AI uses the lifelines library to perform rigorous survival analysis.

Clinical-Grade Features

Kaplan-Meier Estimates

Standard non-parametric statistic used in medical research to estimate the survival function.

Risk Tables

Automatically generate and align "Number at Risk" tables to show sample size at each timepoint.

Log-Rank Tests

Automatically compare survival distributions between groups and display the p-value.

Confidence Intervals

Display 95% confidence intervals (shading) around the survival curves.

Analyze Survival Data

Create publication-quality survival curves with risk tables in seconds.

No account required • Free during beta • Export unlimited