skills.rest
nahisaho
nahisahoCOMMUNITY
Explore all skills by this author →
GitHub

scientific-survival-clinical

Perform Kaplan-Meier, Cox proportional hazards, log-rank, and power analyses on clinical trial data.

3|1|Updated Feb 11, 2026
Search Tags:#power-analysis#survival-analysis#clinical-trials#safety-analysis#kaplan-meier#log-rank#cox-ph
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-survival-clinical
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-survival-clinical
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-survival-clinical
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-survival-clinical

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a robust survival analysis and clinical statistics workflow for clinical trial data, enabling reproducible analysis of time-to-event outcomes and safety metrics.

Core Features & Use Cases

  • Kaplan-Meier estimation with survival curves and log-rank comparison.
  • Cox proportional hazards modeling to estimate hazard ratios and covariate effects.
  • Power analysis and sample size calculations for trial design, including interim monitoring.
  • Safety analysis metrics such as NNT and NNH.

Quick Start

Run the survival-analysis pipeline on your clinical-trial dataset to compute Kaplan-Meier curves, Cox PH results, power analyses, and interim monitoring support.

Frequently Asked Questions about scientific-survival-clinical

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform Kaplan-Meier estimation and log-rank testing on clinical trial data?▼

Kaplan-Meier estimation and log-rank testing on clinical trial data are performed using the Skill's Python implementations, which compute survival curves and compare groups, outputting reproducible results to CSV files and reports.

What is the best way to calculate sample size and power analysis for survival analysis?▼

Power analysis and sample size calculations for survival analysis are handled by the Skill's workflow, which supports trial design optimization and includes Bayesian interim analysis support for ongoing monitoring.

Can I use Cox proportional hazards modeling to estimate hazard ratios with this workflow?▼

Cox proportional hazards modeling is fully supported to estimate hazard ratios and covariate effects, providing robust survival analysis on clinical trial data through reproducible Python implementations.

How do I calculate safety metrics like NNT and NNH for clinical trials?▼

Safety analysis metrics like NNT and NNH for clinical trials are calculated using the Skill's Python implementations, which process time-to-event outcomes and output the resulting safety calculations to CSV reports.

Does the survival analysis pipeline support interim monitoring for clinical trials?▼

Interim monitoring for clinical trials is supported through Bayesian interim analysis, allowing you to perform ongoing power analysis and safety assessments during the trial.

Related Skills

Explore More Data & Analytics Skills→
inflexa-aiinflexa-ai
29

pkpd-clinical-response

Analyze pharmacokinetic and pharmacodynamic data with RECIST 1.1 response classification.

Official
Advanced
tondevreltondevrel
19

lifelines

Analyze right-censored time-to-event data with Kaplan-Meier curves and Cox regression.

Community
Advanced
sencersoylusencersoylu

survival-analysis

Estimate survival curves, compare groups, and model hazards with Kaplan-Meier, log-rank, and Cox methods.

Community
Intermediate
CentauriounCentaurioun

tooluniverse-statistical-modeling

Fit biomedical regression models and compute effect estimates with statsmodels and lifelines.

Community
Advanced