Does it support parallel processing for simulations?

Yes, the skill includes patterns for configuring the proc.load parameter in Mediana to utilize parallelization, significantly speeding up large-scale simulations.

Is this skill suitable for non-statisticians?

While the underlying framework is statistical, the skill provides clear patterns and templates that make it easier for clinical researchers to structure and run simulations without deep R expertise.

What is the Mediana Fundamentals skill?

It is a specialized capability for Claude Code that provides domain-specific guidance and implementation patterns for using the Mediana R package to simulate clinical trials.

Can this skill handle complex trial designs?

Yes, it supports advanced designs including time-to-event endpoints, correlated outcomes, and complex multiplicity adjustment procedures like gatekeeping and graphical approaches.

What are the main components of a Mediana simulation?

The framework consists of three main components: Data Models (data generation process), Analysis Models (statistical methods), and Evaluation Models (success criteria/power).

Mediana Clinical Trial Simulation

Name: Mediana Clinical Trial Simulation
Author: choxos

bychoxos

0•

数据科学与机器学习

Streamlines clinical trial design and simulation using the Mediana framework for Clinical Scenario Evaluation (CSE).

The Mediana Fundamentals skill empowers biostatisticians and clinical researchers to build robust Clinical Scenario Evaluation (CSE) frameworks within Claude. It provides structured guidance for defining complex data models with diverse outcome distributions, configuring analysis models with specialized statistical tests, and implementing advanced multiplicity adjustment procedures such as gatekeeping and chain procedures. By automating the setup of evaluation criteria and simulation parameters, this skill helps users efficiently calculate power metrics and generate comprehensive trial simulation reports to inform critical drug development decisions.

主要功能

01Optimize simulation performance with parallel processing and reproducibility controls.

02Define multi-arm data models with various distributions including Normal, Poisson, and Survival (PFS/OS).

030 GitHub stars

04Implement complex multiplicity adjustments including Fixed-sequence, Gatekeeping, and Chain procedures.

05Configure statistical analysis models with built-in tests like Log-rank, T-Tests, and Wilcoxon.

06Calculate trial success metrics using marginal, disjunctive, and conjunctive power criteria.

使用场景

01Simulating time-to-event outcomes with custom enrollment and dropout distributions.

02Designing Phase II/III clinical trials with complex endpoints and multiplicity constraints.

03Evaluating trial power and success probabilities across multiple treatment effect scenarios.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent mediana-fundamentals

For use in Claude.ai and ChatGPT

主要功能

01Optimize simulation performance with parallel processing and reproducibility controls.

02Define multi-arm data models with various distributions including Normal, Poisson, and Survival (PFS/OS).

030 GitHub stars

04Implement complex multiplicity adjustments including Fixed-sequence, Gatekeeping, and Chain procedures.

05Configure statistical analysis models with built-in tests like Log-rank, T-Tests, and Wilcoxon.

06Calculate trial success metrics using marginal, disjunctive, and conjunctive power criteria.

使用场景

01Simulating time-to-event outcomes with custom enrollment and dropout distributions.

02Designing Phase II/III clinical trials with complex endpoints and multiplicity constraints.

03Evaluating trial power and success probabilities across multiple treatment effect scenarios.