sdtm-programming

SDTM data transformations across Python, SAS, and R are implemented using reusable ETL patterns for domain-level mappings, controlled terminology validation, and study-day calculations. This approach provides structured guidance for reproducible clinical trial dataset processing.

Handling CDISC controlled terminology in SDTM ETL pipelines involves applying terminology mappings and data quality checks directly within the transformation workflow. This enforces standard validation rules across target domains during data ingestion.

Deriving EPOCH and study-day calculations for SDTM domains requires applying standard date handling and epoch derivation logic within the transformation pipeline. This ensures correct temporal relationships across DM and AE clinical trial datasets.

Pandas and Polars can be used for end-to-end SDTM domain mapping specifications by adapting mapping_spec and reference_data to your source dataset. This allows you to scaffold transformations for target domains like DM, AE, and VS.

This SDTM programming approach supports BLFL derivations and metadata generation as part of end-to-end pipelines. It covers the full workflow from raw clinical data ingestion to target dataset generation with proper domain relationships.