Is this compatible with both Stan and JAGS?

Yes, the skill provides specific code blocks and parameterizations tailored for both the Stan and JAGS probabilistic programming languages.

What types of survival models does this skill support?

The skill supports a variety of models including Exponential, Weibull, Log-Normal, Piecewise Exponential (Cox-like), and Frailty models with random effects.

Does it handle censored data?

Yes, it includes specific implementation patterns for right-censored data using the lccdf (log complementary cumulative distribution function) in Stan and dinterval in JAGS.

Does it support group-level random effects?

Yes, the Frailty Model section provides a non-centered parameterization for group-level random effects to handle hierarchical or clustered survival data.

Can I calculate Hazard Ratios (HR) with this skill?

Yes, the skill provides examples of 'generated quantities' blocks in Stan to automatically calculate Hazard Ratios and median survival times from model parameters.

Bayesian Survival Analysis Models

Name: Bayesian Survival Analysis Models
Author: choxos

bychoxos

0•

데이터 과학 및 ML

Implements advanced Bayesian survival and time-to-event models with support for censoring and frailty effects.

This skill provides a comprehensive toolkit for performing Bayesian survival analysis within Claude. It includes implementation patterns for common hazard models such as Exponential, Weibull, and Log-Normal, as well as complex Piecewise Exponential and Frailty models for group-level random effects. Designed for biostatisticians and data scientists, it streamlines the creation of Stan and JAGS code, helping users handle right-censored data, calculate hazard ratios, and estimate survival functions with robust Bayesian uncertainty quantification.

주요 기능

01Support for multiple distributions including Exponential, Weibull, and Log-Normal

02Pre-configured generated quantities for Hazard Ratios (HR) and Median Survival

030 GitHub stars

04Handling of right-censored data using Stan and JAGS syntax

05Implementation of Piecewise Exponential models for flexible hazard shapes

06Frailty models for capturing group-level random effects

사용 사례

01Predicting equipment failure and reliability in industrial engineering

02Analyzing clinical trial data to estimate treatment effects over time

03Modeling customer churn and retention for subscription-based services

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent survival-models

For use in Claude.ai and ChatGPT

주요 기능

01Support for multiple distributions including Exponential, Weibull, and Log-Normal

02Pre-configured generated quantities for Hazard Ratios (HR) and Median Survival

030 GitHub stars

04Handling of right-censored data using Stan and JAGS syntax

05Implementation of Piecewise Exponential models for flexible hazard shapes

06Frailty models for capturing group-level random effects

사용 사례

01Predicting equipment failure and reliability in industrial engineering

02Analyzing clinical trial data to estimate treatment effects over time

03Modeling customer churn and retention for subscription-based services

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent survival-models

For use in Claude.ai and ChatGPT