PHUSE EU Connect

Author: Kristin Kelly

The imminent release of the CDISC SDTMIG v4.0 / SDTM v3.0 represents a pivotal moment for SDTM standards. As a major version update, it introduces significant changes that sponsors should begin preparing for now. Key updates include support for Multiple Subject Instances (MSI), the transition from SUPPQUAL to Non-Standard Variables (NSVs), and metadata restructuring aligned with SDTM Model v3.0. The –BLFL variable has been removed and Sections 1–4 have been reorganized for improved clarity and usability. New domains such as Event Adjudication (EA), along with five additional body system domains, further expand representational capabilities. Updates to domains such as Protocol Deviations (DV) and the addition of the Laboratory Results Conventional (LC) domain facilitate alignment with regulatory expectations. Also notable, splitting the Disease Response (RS) domain into RS and a new Clinical Classifications (CC) domain. This paper explores what’s new in SDTMIG v4.0 and provides practical guidance to help sponsors navigate this era of transition.

Authors: Julie Ann Hood and Wendy Young

Behind every high-quality, CDISC-compliant SDTM dataset lies a team of data-driven heroes, each with their own unique skillsets and battle-tested expertise. In today’s clinical trials, creating and validating submission-ready datasets is rarely a solo mission. It’s a full-scale, multi-role operation that calls upon the powers of Data Managers, Standards Managers, Clinical Programmers, and Biostatisticians, each bringing something special to the fight for clean, conformant data.

Our heroes may hail from different domains, but they share a common trait: a working knowledge of CDISC standards. Whether it’s SDTM variables on an aCRF, CDASH-structured EDC systems, or routine exposure to standardized specs, each role is already familiar with the tools of the trade. This foundational knowledge becomes their secret weapon: breaking down long-standing silos and enabling smarter, more collaborative workflows than ever before.

In a world of evolving standards, AI-assisted processes, and increasing expectations for speed and accuracy, the path to submission success isn’t always linear. There’s no single “right way” to create SDTM datasets, but there are more efficient ways. Understanding the strengths of each role opens up opportunities to rethink traditional processes, distribute tasks more effectively, and innovate workflows that better leverage individual expertise.

This poster will present our SDTM Squad, outlining the signature skills and superpowers each role brings to the clinical trial data lifecycle, supercharged by emerging contributions of AI. By highlighting the strengths of this dynamic team, we’ll demonstrate that with the right strategy and collaboration, regulatory compliance can be both faster and more heroic.

Authors: Seiko Yamazaki & Hiromi Okada

This presentation builds on prior work emphasizing the importance of understanding the ‘Roles of Each Sheet and Column’ in Define.xml Excel specifications for quality and regulatory compliance (https://www.lexjansen.com/phuse/2025/SA/PAP_SA08.pdf.). It proposes a hybrid framework combining Pinnacle 21 Community, large language models (LLMs), and human review to support efficient Define-XML v2.1 development for SDTM and ADaM. The workflow generates Excel specifications from completed datasets, then uses LLMs to augment sections such as variable descriptions and derivations based on structured inputs including implementation guides, SAPs, shells, and detailed column-level guidance. This improves consistency and accuracy of metadata generation. Critical elements requiring interpretability and regulatory judgment remain human-reviewed. Applied to a clinical study, the approach reduced manual effort for descriptive metadata while maintaining validation quality, demonstrating that a Human–LLM–tool hybrid can enhance efficiency while preserving regulatory readiness.