Does it include visualization tools?

Yes, it provides code for publication-quality Kaplan-Meier plots, risk tables, forest plots, and cumulative incidence curves using survminer and ggsurvplot.

Which R packages does this skill support?

The skill supports core packages like survival and survminer, alongside specialized tools like flexsurv, tidycmprsk, survRM2, and the censored package for tidymodels integration.

Is it compatible with tidymodels?

Absolutely, it includes specific patterns for the 'censored' package to integrate survival regression into a tidy machine learning workflow.

Can this skill handle competing risks?

Yes, it includes patterns for cause-specific hazards and Fine-Gray subdistribution hazards using both cmprsk and tidycmprsk frameworks.

R Survival Analysis Patterns

Name: R Survival Analysis Patterns
Author: choxos

bychoxos

0•

数据科学与机器学习

Implements comprehensive survival analysis workflows in R using tidy and traditional biostatistical frameworks.

This skill provides a standardized collection of survival analysis methods for R, ranging from basic Kaplan-Meier estimation and Cox proportional hazards models to advanced techniques like competing risks and multi-state modeling. It bridges the gap between traditional biostatistical packages like 'survival' and modern 'tidymodels' workflows, offering production-ready patterns for clinical research, longitudinal studies, and time-to-event data analysis. Whether you need publication-quality visualizations with 'survminer' or flexible parametric models with 'flexsurv', this skill enables Claude to generate accurate, best-practice statistical code for complex health and data science scenarios.

主要功能

01Semi-parametric Cox Proportional Hazards modeling with assumption diagnostics

02Parametric and flexible spline-based models (AFT, Weibull, Royston-Parmar)

03Integration with 'tidymodels' and 'censored' for modern ML workflows

040 GitHub stars

05Non-parametric estimation using Kaplan-Meier and log-rank testing

06Competing risks analysis and multi-state model implementations

使用场景

01Analyzing clinical trial data to compare treatment efficacy and hazard ratios

02Predicting customer churn or equipment failure in industrial time-to-event settings

03Generating publication-ready survival visualizations and forest plots for medical research

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent survival-analysis

For use in Claude.ai and ChatGPT

主要功能

01Semi-parametric Cox Proportional Hazards modeling with assumption diagnostics

02Parametric and flexible spline-based models (AFT, Weibull, Royston-Parmar)

03Integration with 'tidymodels' and 'censored' for modern ML workflows

040 GitHub stars

05Non-parametric estimation using Kaplan-Meier and log-rank testing

06Competing risks analysis and multi-state model implementations

使用场景

01Analyzing clinical trial data to compare treatment efficacy and hazard ratios

02Predicting customer churn or equipment failure in industrial time-to-event settings

03Generating publication-ready survival visualizations and forest plots for medical research