Tidy ITC Workflow

About

This skill streamlines the creation and maintenance of Indirect Treatment Comparison (ITC) analysis pipelines by applying the 'Tidy Modeling with R' (TMwR) philosophy to health economics and outcomes research. It provides a standardized architecture for data validation, preparation, analysis, and reporting, ensuring that complex ITC methods—such as MAIC, STC, and NMA—are implemented with consistent interfaces and robust reproducibility. By leveraging this workflow, researchers and data scientists can reduce coding errors, enhance pipeline maintainability, and satisfy the rigorous documentation requirements typical of Health Technology Assessment (HTA) submissions.

Key Features

• Automated data validation patterns for IPD and Aggregate Data (AgD)
• Unified analysis interface for MAIC, STC, NMA, and ML-NMR
• Tidy modeling 'recipe' patterns for covariate centering and preparation
• Built-in reproducibility tools for seed management and session documentation
• Standardized ITC project architecture and folder structures
• 0 GitHub stars

Use Cases

• Building reproducible R pipelines for clinical trial and real-world evidence analysis
• Standardizing interfaces across disparate ITC methods for easier comparison
• Setting up a new HTA-compliant indirect treatment comparison project