What is the 'Pit of Success' philosophy in this skill?

It is a design principle where the workflow and interface naturally guide users toward winning practices, such as mandatory data validation and consistent naming, making the correct methodological path the easiest one to follow.

Which ITC methods does this workflow support?

The skill provides patterns and interfaces for Pairwise Meta-Analysis (MA), Network Meta-Analysis (NMA), Matching-Adjusted Indirect Comparison (MAIC), Simulated Treatment Comparison (STC), and Multilevel Network Meta-Regression (ML-NMR).

Does it handle different data types?

Yes, it includes specific validation patterns and preparation recipes for Individual Patient Data (IPD), Aggregate Data (AgD), binary outcomes, and survival (time-to-event) data.

How does it ensure analysis reproducibility?

It enforces strict seed management for stochastic operations, utilizes renv for package version control, and includes standardized functions to document session information within the analysis output.

Tidy ITC Analysis Workflow

Name: Tidy ITC Analysis Workflow
Author: choxos

bychoxos

0•

Data Science & ML

Standardizes Indirect Treatment Comparison (ITC) analyses in R using tidy modeling principles and reproducible workflow patterns.

This skill provides a comprehensive framework for conducting Indirect Treatment Comparisons (ITC) in R, strictly adhering to the 'Tidy Modeling with R' (TMwR) philosophy. It enables biostatisticians and researchers to build robust, maintainable analysis pipelines by enforcing consistent function interfaces, automated data validation for IPD and aggregate outcomes, and standardized result formats. By integrating best practices for reproducibility—including strict seed management and package versioning—it ensures that complex models like MAIC, STC, and NMA are transparent, audit-ready, and easier to review.

Key Features

01Standardized function signatures for MAIC, STC, and NMA methods

02Automated data validation for individual patient and aggregate data structures

03TMwR-aligned 'Pit of Success' architecture for biostatistical modeling

04Consistent result tibble formatting for seamless reporting and visualization

05Reproducible environment setup with renv integration and seed management

060 GitHub stars

Use Cases

01Standardizing analysis interfaces across multiple population-adjusted methods

02Validating data integrity for survival and binary outcomes in biostatistical projects

03Building reproducible ITC pipelines for clinical trial evidence synthesis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent tidy-itc-workflow

For use in Claude.ai and ChatGPT

Key Features

01Standardized function signatures for MAIC, STC, and NMA methods

02Automated data validation for individual patient and aggregate data structures

03TMwR-aligned 'Pit of Success' architecture for biostatistical modeling

04Consistent result tibble formatting for seamless reporting and visualization

05Reproducible environment setup with renv integration and seed management

060 GitHub stars

Use Cases

01Standardizing analysis interfaces across multiple population-adjusted methods

02Validating data integrity for survival and binary outcomes in biostatistical projects

03Building reproducible ITC pipelines for clinical trial evidence synthesis