MediStat Cox
Hazard ratios adjusted for other factors
Estimate adjusted hazard ratios with Cox regression, assess multicollinearity and check the proportional-hazards assumption.
What to prepare
Time-to-event data and prespecified covariates
What you get
Univariate and multivariable HR tables · VIF · Schoenfeld diagnostics
Requirements
Python · lifelines, statsmodels, pandas, numpy, matplotlib, openpyxl
Python setup guide ↗Interpreting the results
Specify prespecified adjustment variables so that confounder selection does not rely solely on the default univariate p-value screening.
Install in Claude Code
Enter these commands in the Code view of the Claude app or the Claude Code panel in VS Code. These are installation commands, not prompts for a regular Claude chat.
Run the first line, then the second. Restart Claude Code after installation to load the skill.
/plugin marketplace add JeonKH81/MediStat-Cox /plugin install medistat-cox@medistat-cox
These commands register the skill. Python, R and analysis packages must be prepared separately; check the requirements above and the original documentation.
Installation instructions on GitHub ↗Try this prompt after installation
Replace the bracketed fields with your own information, then paste the prompt into Claude Code.
[Goal] Estimate adjusted hazard ratios with Cox regression. [Inputs] File: [data filename] Time / event variables: [names and definitions] Exposure of interest: [variable] Required adjustment variables: [prespecified variables] [Task] Use MediStat Cox to report adjusted HRs, 95% CIs, VIF and proportional-hazards diagnostics. [Checks] Explain the variable-selection plan first. Reflect any violations of the proportional-hazards assumption in the interpretation.
This page provides usage and installation guidance. Analyses and file processing take place in your installed Claude Code environment.