In our earlier SDTM blog, we learned about the basics of understanding SDTM in clinical trials. In the second part of the blog, now will try and understand about the versions of SDTM in clinical trials, its datasets, its domains, and SDTM mapping. Let’s go!
Versions of SDTM in clinical trials
SDTM has evolved over the years to keep pace with the constantly changing clinical research field. From its initial release to the latest version, SDTM v2.0, the standard has undergone seven iterations (v1.0, v1.4, v1.5, v1.6, v1.7, v1.8, v2.0) to address emerging regulatory requirements and industry best practices. Each new version of SDTM in clinical trials introduce enhancements and clarifications to improve data consistency, usability, and interoperability.
For instance, SDTM 1.8 includes updates to existing domains, such as the addition of new variables and the refinement of existing ones, to better capture and represent clinical trial data. These updates are informed by feedback from stakeholders, including pharmaceutical companies, regulatory agencies, and academic researchers, ensuring that SDTM remains relevant and effective in a rapidly evolving regulatory landscape.
Adoption of SDTM in clinical trials continues to grow, with an increasing number of organizations recognizing the benefits of standardized data formats in streamlining clinical trial processes. According to industry reports, a significant percentage of regulatory submissions to the FDA now utilize SDTM, underscoring its importance in regulatory compliance and submission readiness.
Staying up to date with the latest version of SDTM is essential for sponsors, contract research organizations (CROs), and other stakeholders involved in clinical research. By leveraging the capabilities of SDTM 2.0 and future iterations, organizations can enhance data quality, expedite regulatory submissions, and ultimately accelerate the development of new therapies for patients in need.
Datasets of SDTM in clinical trials
SDTM datasets are nothing but the data tables that are created in line with the SDTM standard for regulatory submission. Each standardized dataset serves a specific purpose in data representation and analysis. These datasets cover various aspects of a clinical trial, from subject demographics to adverse events, and are designed to facilitate data aggregation, analysis, and reporting.
For example, the Trial Summary dataset provides an overview of key study parameters, while the Adverse Events dataset captures safety-related information. The adoption of SDTM datasets is widespread, with a growing number of pharmaceutical companies and CROs embracing the standard to improve data quality and regulatory compliance.
By adhering to the guidelines of SDTM in clinical trials, organizations can ensure consistency and interoperability across studies, making it easier to exchange data with regulatory agencies and research partners. Furthermore, SDTM datasets enable more efficient data analysis and decision-making, ultimately expediting the drug development process.
Domains of SDTM in clinical trials
Within the framework of SDTM, data is organized into distinct domains, each representing a specific aspect of the clinical trial process. These domains provide a standardized structure for capturing and reporting data, ensuring consistency and interoperability across studies. Common SDTM domains include Demographics, Exposure, Concomitant Medications, and Laboratory Results, among others. Each domain serves a unique purpose in capturing different types of data relevant to the study, such as subject characteristics, treatment exposure, and laboratory measurements.
Adherence to SDTM domains is critical for regulatory compliance and submission readiness. By structuring data according to predefined domains, sponsors and researchers can streamline data collection, analysis, and reporting, thus reducing the risk of errors and discrepancies. A significant percentage of clinical trials have adopted SDTM domains to enhance data quality and regulatory compliance.
By embracing SDTM domains, organizations can simplify data management processes, facilitate collaboration, and accelerate the development of new therapies. Adherence to SDTM standards remains essential for ensuring the integrity and reliability of clinical trial data. By standardizing data across studies and stakeholders, SDTM domains contribute to the advancement of medical science and the improvement of patient outcomes.
What is SDTM Mapping?
One of the main challenges in implementing SDTM in clinical trials is mapping data from diverse sources to the standardized format. It involves the process of transforming data from its original format into SDTM-compliant datasets and domains. It also requires careful consideration of data elements, mappings, and transformations to ensure accuracy and completeness.
Advanced mapping tools and techniques, such as automation and metadata-driven mapping, can streamline this process and reduce manual effort. A number of clinical trials face challenges related to data mapping and standardization. However, organizations that invest in robust mapping strategies and tools can overcome these challenges effectively, ensuring compliance with SDTM standards and regulatory requirements.
By adopting best practices in SDTM mapping, sponsors and researchers can accelerate the data conversion process, reduce errors, and improve data quality. This, in turn, enhances the efficiency of clinical trial operations and expedites the delivery of new treatments to patients in need.
Conclusion
SDTM in clinical trials serves as a cornerstone of data standardization, promoting consistency, interoperability, and regulatory compliance. By adopting the standard, stakeholders can streamline data management processes, enhance data quality, and accelerate the drug development lifecycle. Embracing SDTM not only benefits individual studies but also contributes to broader initiatives aimed at harmonizing clinical trial data on a global scale. As the industry continues to embrace digital transformation, adherence to SDTM standards will play an increasingly pivotal role in driving innovation and advancing evidence-based medicine. We will learn more about SDTM in clinical trials in another blog.
At Inductive Quotient Analytics (IQA), we are committed to advancing clinical trials with our innovative offerings and help medical interventions reach patients faster. We have also efficiently handled clinical trials across therapeutics for many global organizations. To know how our specialized services and solutions can help your clinical trials, write to us at hello@inductivequotient.com.