Automation

The life sciences industry is seeing more requests for quick, compliant clinical trial submissions, which makes it even more important to improve programming operations. Generative artificial intelligence, especially large language models, could change how SDTM, ADaM, and TLFs are created in a big way.

This presentation investigates practical applications of generative AI to automate and enhance clinical programming duties, from mapping raw data to SDTM domains to crafting ADaM specifications and generating common code or statistical summaries. The examples are aimed at reducing manual effort while preserving traceability, compliance, and productivity in SAS-based environments.

Matt Becker is an Advisory Industry Consultant with the SAS Health and Life Science division. His more than 30 years of life science experience include over 8 years with SAS, concentrating on next-generation clinical trials, data management, analysis, advanced analytics, and deployment options in life sciences.

Symphony is a web application designed to simplify the creation and management of both mock shells and final outputs for tables, figures, and listings. Unlike static Word documents, it streamlines design, review, and standardization by connecting conceptualization directly to computation.

Using reusable, standardized analysis blocks, Symphony automatically generates both mock shells and final outputs while enforcing the creation and reuse of CDISC ADaM metadata, supporting Analysis Results Metadata, and enabling exportable code for reproducibility. The platform also includes validation tracking, versioning, and metrics to improve transparency, traceability, quality, and timeline efficiency in clinical reporting.

Karma Tarap is a seasoned professional with 20 years of experience in the pharmaceutical industry, specializing in innovation within statistical programming. With degrees in immunology, microbiology, bioinformatics, and data science, he brings a unique blend of expertise to his work.