When it comes to clinical trial data, consistency is everything. That’s where the Study Data Tabulation Model (SDTM), developed by the Clinical Data Interchange Standards Consortium (CDISC), comes in.
SDTM standardizes how data is organized, making it easier for sponsors, CROs, and regulatory authorities like the FDA to review and approve studies faster.
Key Components and Benefits of SDTM
- Standardized Datasets and Variables: SDTM organizes clinical data into specific domains- such as demographics, adverse events, and laboratory results; with clear naming conventions, supporting easier analysis and comparison across studies.
- Controlled Terminology: By using established medical dictionaries like MedDRA and WHO Drug, SDTM ensures consistency in coding medical terms, improving accuracy and reliability.
- Traceability and Data Quality: SDTM structures enable clear traceability from the original data source through final analysis, ensuring transparency and strengthening data integrity.
- Regulatory Compliance: Following SDTM standards streamlines the regulatory submission process, reducing the risk of rework or approval delays.
The Broader Impact: SDTM facilitates collaboration among sponsors, CROs, and regulators, improving data quality, accelerating review timelines, and ultimately supporting faster development of new therapies.
Challenges in Raw to SDTM Conversion
Despite its benefits, converting raw clinical data into SDTM format remains a complex and resource-intensive process.
Traditional methods often rely heavily on manual effort, making the process time-consuming, labor-intensive, and susceptible to errors.
Even semi-automated approaches, while an improvement, still require significant manual intervention. Variability in raw data formats, evolving CDISC standards, and the need for continuous maintenance further complicate the conversion process.
There is a clear need for a more efficient and scalable solution- and this is where generative AI (GenAI) offers significant value.
By automating key aspects of SDTM development, GenAI-driven frameworks can enhance efficiency, improve data quality, and allow clinical teams to focus on higher-value activities.
Advancing SDTM Conversion with GenAI
At Saama, we have developed SDTM-RAG- an AI-powered application purpose-built to automate and streamline SDTM mapping.
Here’s how SDTM-RAG works:
- Step 1: Source Data Understanding
SDTM-RAG begins by analyzing the metadata and unique values of the source data. This analysis generates a comprehensive “mental map” of the dataset, identifying the relationships between different variables.
- Step 2: Intelligent Mapping
The first AI agent, powered by a knowledge base of SDTM Implementation Guides, matches source data to the appropriate SDTM domains and variables. It generates source-to-target mappings along with transparent explanations and justifications for each decision.
- Step 3: Independent Validation
A second agent independently reviews and validates the output of the first agent, providing an additional layer of quality control that mirrors human verification processes.
- Step 4: Application of Transformation Functions
A third agent evaluates the validated mappings to identify and apply the necessary transformation functions for SDTM compliance.
- Step 5: Code Generation
Finally, a programming agent translates the complete mappings into well-structured, submission-ready SAS and R programs, aligned with industry best practices.
Key Differentiators of SDTM-RAG
- Transparency: Each recommendation includes detailed reasoning, ensuring complete visibility into the mapping process.
- User Oversight: Users can review, refine, and approve outputs at each stage, maintaining full control.
- Regulatory-Ready Outputs: Digital specifications and code are designed for seamless integration into submission workflows, including defining XML generation.
Transforming SDTM Mapping with AI
By automating complex and repetitive tasks, Saama’s SDTM-RAG fundamentally improves the SDTM conversion process; delivering greater efficiency, higher data quality, and faster submission timelines.
With a GenAI-driven approach that preserves transparency and user oversight, SDTM-RAG enables clinical data teams to operate more strategically, helping bring life-saving therapies to patients more quickly.
Building on our progress with SDTM, we are advancing toward ADaM automation.
Watch this space as we continue to enhance clinical data workflows.
Get in touch with our experts at [email protected] to schedule a personalized demo and streamline your clinical data workflows with AI-powered precision and speed.
