Survival analysis is one of the most requested biomedical workflows and one of the easiest to get subtly wrong. Here is how it runs in an AI workspace. CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI.
The ask
Upload the dataset (CSV, Excel, SPSS, SAS, or Stata all work) and ask: 'run a Kaplan-Meier analysis by treatment arm with a log-rank test, then fit a Cox model adjusting for age and sex.'
What CATO does
- Identifies the time, event, and grouping columns, confirming with you if ambiguous.
- Checks censoring structure and missing data, and states how both are handled.
- Fits the curves and the Cox model in real Python or R and reports test statistics, p-values, confidence intervals, and hazard ratios.
- Returns a publication-quality figure you can restyle in the no-code editor, plus the exact code.
Why the code matters
Journals and reviewers increasingly ask how censoring, ties, and proportional hazards were handled. Because every CATO figure carries its code and assumptions, those answers are already written.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.