Which R packages are supported for survival visualization?

The skill focuses on the survminer package, providing patterns for ggsurvplot to create publication-quality Kaplan-Meier plots with risk tables and p-values.

Is it compatible with modern tidy data principles?

Yes, it includes specific patterns for the censored package, allowing you to fit survival models within the tidymodels framework.

Can I use this for competing risks analysis?

Absolutely. It provides implementations for both Cause-Specific Hazards and Fine-Gray Subdistribution Hazards models using cmprsk and tidycmprsk.

Does this skill handle Proportional Hazards assumption testing?

Yes, it includes patterns for testing the PH assumption using cox.zph and visualizing Schoenfeld residuals to ensure model validity.

R Survival Analysis Patterns

Name: R Survival Analysis Patterns
Author: choxos

bychoxos

Data Science & ML

Implements comprehensive survival analysis and time-to-event modeling patterns using the R statistical programming language.

About

This skill provides specialized guidance for performing survival analysis within the R ecosystem, covering the full spectrum from basic non-parametric methods to advanced multi-state modeling. It offers ready-to-use patterns for Kaplan-Meier estimation, Cox Proportional Hazards modeling, and parametric survival analysis using packages like survival, flexsurv, and rstpm2. Additionally, it includes sophisticated techniques for handling competing risks, Restricted Mean Survival Time (RMST), and modern tidymodels integration via the censored package. It is designed to help data scientists and biostatisticians implement robust, publication-quality longitudinal analyses with automated visualization workflows using survminer.

Key Features

Tidymodels integration for censored regression and machine learning workflows
Advanced competing risks and multi-state model implementations
Parametric and flexible spline-based survival models (Weibull, Gamma, Royston-Parmar)
Comprehensive Kaplan-Meier estimation and log-rank testing patterns
0 GitHub stars
Cox Proportional Hazards modeling with automated assumption diagnostics

Use Cases

Customer churn analysis and time-to-event modeling in business analytics
Predictive maintenance and reliability engineering for industrial equipment failure
Biostatistical analysis for clinical trials and patient survival outcomes

About

Key Features

Tidymodels integration for censored regression and machine learning workflows
Advanced competing risks and multi-state model implementations
Parametric and flexible spline-based survival models (Weibull, Gamma, Royston-Parmar)
Comprehensive Kaplan-Meier estimation and log-rank testing patterns
0 GitHub stars
Cox Proportional Hazards modeling with automated assumption diagnostics

Use Cases

Customer churn analysis and time-to-event modeling in business analytics
Predictive maintenance and reliability engineering for industrial equipment failure
Biostatistical analysis for clinical trials and patient survival outcomes