Data is at the heart of every clinical trial. Every data in the clinical trial impacts the decisions made regarding patient care and research results. However, different contract research organizations (CROs) and other organizations involved in clinical trials may collect data differently.
Additionally, supplemental data can be collected during the trials that don’t fall under any domain described in the data form but still need to be included. As such, it’s common to have inconsistencies in clinical trial data.
Over the past years, relevant bodies and non-profit organizations such as the Federal Drug Administration (FDA) and Clinical Data Interchange Standards Consortium (CDISC) have taken the initiative to develop standards for clinical trial data. The main aim of these data standards is to provide a common platform for organizations across the globe participating in clinical trials and reduce the cost and resources needed.
These clinical trial data standards guide the collection, submission, and reporting of clinical data and have the following benefits:
Create Proper Analysis Of Data And Associated Metadata
One of the biggest problems facing clinical trial data is the proper analysis of datasets and the associated metadata. This can lead to errors in creating a comprehensive picture of the clinical trial and wrong decisions. For instance, clinical trials that test candidate drugs have produced inconstant outcomes, perhaps due to defects in the study design, the sample size, or the duration of the study.
With the help of standardized data, all stakeholders have access to a single platform where they can analyze data quickly and accurately.
The Analysis Data Model (ADaM) model was created to standardize clinical trial data for better and more efficient analysis. ADaM datasets work with Study Data Tabulation Model (SDTM) to provide traceability between the analysis data and the source data.
For example, the SDTM datasets might include data on demographics, medical history, and adverse events, while the ADaM datasets might include data on efficacy and safety endpoints.
By understanding ADaM SDTM, preparing analysis-ready data and facilitating clear communication is more accessible.
Improves Data Quality
By following data standards, clinical trial organizations can ensure their data is collected consistently and reliably. This reduces errors in the data collection process and ensures uniformity across different datasets. Additionally, the data standards provide guidelines for conducting trials and analyzing results, eliminating any doubts or confusion among researchers.
For example, SDTM outlines how data should be collected, organized, and presented. It clearly outlines each dataset’s attributes, content, and structure. Before the SDTM model was created, there was no standardization, which often led to errors. However, with defined domains and variables, it’s easier to ensure data accuracy and reduce errors.
Increased Opportunities For Automation
Automation is a critical part of clinical data analysis. It helps reduce errors and the time needed to collect, clean, submit, and analyze data. For example, electronic case report forms (eCRF) instead of paper-based forms reduce data entry errors and speed up data collection.
Data standards also help in automation by providing a common language for all stakeholders to communicate, allowing them to quickly exchange large amounts of data and automate processes such as analysis and reporting. This accelerates the overall process and reduces costs associated with staffing requirements.
Facilitates Communication Between Stakeholders
The data standards allow stakeholders to always be on the same page and help them better understand each other’s requirements. The standardization of data assists in sharing information across different countries, institutions, and organizations, eliminating any language barriers that may have previously existed.
Another example of how data standards improve communication is the development of the Operational Data Model (ODM). ODM was designed to facilitate the exchange and integration of clinical trial data from different sources in a consistent format. The model ensures that all stakeholders have access to a common platform for communication, allowing them to share information quickly and accurately.
Data And Result Consistency
Clinical trial data standards ensure all stakeholders have access to the same, consistent dataset. This eliminates discrepancies between different datasets and allows stakeholders to get a more comprehensive view of the results. This is especially important when submitting clinical trials for approval or regulatory review.
For example, the FDA requires that investigators report unexpected suspected adverse reactions that occur during clinical trials. Before that, no strict guidelines left a loophole in reporting adverse events.
It’s easier to ensure consistency and accuracy of the data and results across different datasets, allowing for faster review by regulatory agencies. Additionally, it makes reusability, sharing, and cross-referencing data from various sources easier, allowing for more efficient analysis.
Conclusion
Clinical trial data standards provide a level of consistency and accuracy that is essential for the success of any clinical research study. Providing clear guidelines on collecting and organizing data helps ensure that all stakeholders have access to a common language to communicate and exchange data. As research becomes more complex and data-driven, these standards will be increasingly crucial in regulating clinical trials and improving their accuracy.