At the different stages of a clinical trial, proper collection, management, and analysis of data are paramount. The Study Data Tabulation Model (SDTM) is quintessential for organizing and presenting clinical trial data.
As data complexities grow with the vast amounts of unstructured collected data and regulatory requirements evolve, the manual process of transforming source data into SDTM format becomes increasingly cumbersome.
However, a powerful combination of Artificial Intelligence (AI), Machine Learning (ML), and metadata repositories can revolutionize this process, making SDTM transformation more efficient, accurate, and adaptable than ever before.
This blog post explores the use of metadata repositories in SDTM transformation, metadata setup, and how AI and ML can automate metadata repository setup and foster SDTM transformation metadata setup for a study.
Robust MDR framework
To effectively automate the SDTM transformation process using AI and ML, a robust metadata repository (MDR) framework is key. This framework serves as a centralized hub that captures and manages metadata related to clinical studies, source data, SDTM domain metadata, SDTM mapping specifications, and transformation rules.
Moreover, it enhances collaboration and consistency among team members by providing a single source of truth for data transformations. It provides inheritability and scalability for future study creation. It maintains data integrity and traceability, streamlining the validation process, and expediting regulatory submission. The MDR ensures transparency and allows for efficient management of changes, ultimately leading to an accurate and compliant SDTM dataset.
MDR using AI and ML
Let us understand the crucial steps in leveraging AI and ML to create efficient MDRs
In ML algorithms, historical transformation rules will be trained and applied automatically to new studies. These algorithms can identify patterns in the source data and generate transformation rules for variables with similar characteristics.