Evidence Behind the Automation of Clinical Trial Statistical Programming: A Scoping Review of Technology Adoption, Validation Frameworks, and AI/ML Integration (2020-2025)

Background: Clinical trial statistical programming is transitioning from manual, study-specific coding toward metadata-driven, automated pipelines. Although general data management transformation has been reviewed, comprehensive synthesis of statistical programming automation--particularly tables, listings, and figures (TLF) generation and validation frameworks--remains limited. This review addresses this gap through systematic evidence synthesis. Methods: We conducted a structured literature review across PubMed, Google Scholar, arXiv, and industry conference proceedings (PharmaSUG, PHUSE, R/Pharma) from January 2020 to December 2025. We applied GRADE methodology to assess evidence quality.