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survival-analysis

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

Updated Mar 19, 2026
Search Tags:#survival-analysis#clinical-research#time-to-event#kaplan-meier#cox-model#biostatistics#log-rank
One-click install
npx skills add https://github.com/sencersoylu/scholar-flow --skill survival-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: survival-analysis
Source: https://github.com/sencersoylu/scholar-flow/tree/main/skills/statistics/survival-analysis
Command: npx skills add https://github.com/sencersoylu/scholar-flow --skill survival-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes time-to-event data (survival data) by estimating survival curves, comparing groups, and modeling hazards to inform clinical or research decisions.

Core Features & Use Cases

  • Kaplan-Meier estimation for time-to-event probabilities
  • Log-rank tests for group comparisons
  • Cox proportional hazards modeling for risk factors
  • Use Case: evaluating treatment effect on time to relapse in oncology trials.

Quick Start

Use this skill to perform a Kaplan-Meier analysis on a cohort with time-to-event data and censoring information.

Frequently Asked Questions about survival-analysis

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

FAQPage Schema
How do I perform Kaplan-Meier estimation on clinical trial time-to-event data?▼

Kaplan-Meier estimation analyzes time-to-event data by calculating survival probabilities across a cohort while properly accounting for patient censoring. This skill processes clinical datasets to estimate survival curves for multiple comparison groups.

Can I use Cox proportional hazards modeling to identify risk factors in biostatistics datasets?▼

Cox proportional hazards modeling evaluates risk factors by calculating hazard ratios and confidence intervals from time-to-event datasets. This skill supports model diagnostics to validate covariate assumptions in biostatistics research.

What is the best way to compare survival curves between multiple groups?▼

Log-rank tests compare survival curves between multiple groups by testing the null hypothesis of no difference in time-to-event outcomes. This skill calculates statistical significance for group comparisons in clinical research datasets.

Does time-to-event analysis work with censored data in oncology trials?▼

Time-to-event analysis handles censored data by incorporating incomplete observation periods into Kaplan-Meier estimation and Cox models. This skill evaluates treatment effects on relapse times in oncology trials with right-censored patient records.

When do I need Cox model diagnostics for survival analysis?▼

Cox model diagnostics validate proportional hazards assumptions by testing covariate independence and time-dependent effects in survival models. This skill generates diagnostic outputs to ensure hazard ratio estimates remain statistically valid.

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