What R packages does this skill support?

This skill primarily provides implementation patterns for the simtrial and Mediana packages, which are industry standards for survival and clinical trial simulations.

Can this skill help with adaptive trial designs?

Yes, it includes frameworks for sample size re-estimation and concepts for response-adaptive randomization to improve trial efficiency.

Is this skill suitable for regulatory trial planning?

While it provides best practices and common patterns, it should be used to supplement professional biostatistical analysis and validated against specific agency expectations.

Does it handle multiple primary endpoints?

Absolutely, it covers co-primary, hierarchical, and gatekeeping strategies to manage multiplicity and ensure statistical power.

Clinical Trial Design Patterns

Name: Clinical Trial Design Patterns
Author: choxos

bychoxos

0•

Data Science & ML

Implements and optimizes complex clinical trial designs including multi-arm, adaptive, and stratified patterns for biostatistical research.

This skill provides specialized guidance for biostatisticians and clinical researchers to design robust clinical trials using industry-standard R packages like simtrial and Mediana. It covers a wide range of sophisticated patterns such as dose-finding, co-primary endpoints, biomarker-defined subgroups, and event-driven designs, offering both implementation code and decision-making frameworks to ensure statistical validity and regulatory alignment. By providing standardized code snippets and a design selection flowchart, it streamlines the transition from clinical objectives to technical implementation.

Key Features

01Stratified randomization and biomarker subgroup analysis

02Multi-arm and dose-finding design implementations

030 GitHub stars

04Complex multi-endpoint and gatekeeping strategies

05Adaptive design frameworks for sample size re-estimation

06Event-driven and time-to-event simulation patterns

Use Cases

01Selecting the optimal trial design based on specific clinical objectives and endpoints

02Planning hierarchical or co-primary endpoint analysis models for regulatory submissions

03Implementing unequal randomization or active comparator designs in R

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent clinical-trial-design-patterns

For use in Claude.ai and ChatGPT

Key Features

01Stratified randomization and biomarker subgroup analysis

02Multi-arm and dose-finding design implementations

030 GitHub stars

04Complex multi-endpoint and gatekeeping strategies

05Adaptive design frameworks for sample size re-estimation

06Event-driven and time-to-event simulation patterns

Use Cases

01Selecting the optimal trial design based on specific clinical objectives and endpoints

02Planning hierarchical or co-primary endpoint analysis models for regulatory submissions

03Implementing unequal randomization or active comparator designs in R

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent clinical-trial-design-patterns

For use in Claude.ai and ChatGPT