CDISC stands for Clinical Data Interchange Standards Consortium, and SDTM stands for Study Data Tabulation Model.
CDISC is the organization that develops a set of universal standards to support crucial activities such as data acquisition, exchange, submission and archival in clinical research, benefiting all the stakeholders involved (such as researchers, technology vendors and pharma companies) in the end-to-end data management process.
There are four CDISC standard groups:
i) Foundational –are core standards that provide the fundamental principles and framework for CDISC standards development and implementation, which includes SHARE (Shared Health and Research Electronic Library), Protocol Representation Model (PRM), Define-XML, CDISC 3D (Define, Design, Develop).
ii) Data exchange – focus on the standardization of data to support the electronic exchange of clinical trial data between different stakeholders. This includes SDTM (Study Data Tabulation Model), ADaM (Analysis Data Model), SEND (Standard for the Exchange of Nonclinical Data)
iii) Therapeutic areas – developed to address specific therapeutic domains within clinical research. They include standards such as, CDASH (Clinical Data Acquisition Standards Harmonization), CDISC ADaM for Pharmacokinetics (PK), CDISC Oncology
iv) Controlled terminology – provides standardized terminology and definitions for clinical concepts and variables used in CDISC standards, across therapeutic areas. It ensures that terms used in clinical data are consistent and well-defined.
SDTM is one of the first created and most used CDISC standards, mainly related to compiling the collected data for future analysis. SDTM streamlines data to be aggregated, mined, reused, reviewed and shared. It helps in faster due diligence and regulatory approvals.
Compliance in Clinical Data Management (CDM) Practice:
Compliance with the CDISC/SDTM standards is rewarding at different clinical data management stages. The following are the foundational standards related to the CDM process,
Planning: The Protocol Representation Model (PRM) is a standard CDISC created with over 300 common protocol elements for the planning phase. It helps in Case Report Form (CRF) and study protocol creation.
Example: PRM helps create a clear plan for a clinical trial. It includes information like what data needs to be collected, how often patients need to visit the clinic, and how the data will be analysed. This ensures everyone follows the same plan.
Data Collection: Clinical Data Acquisition Standards Harmonization (CDASH) provides guidelines on filling the fields in a universal format.
Example: When measuring a patient’s blood pressure, CDASH ensures that the systolic and diastolic readings, along with the date and time, are all recorded in a standardized units or naming conventions.
Data Exchange: Operational Data Model (ODM) enables data and metadata movement between CDM stages.
Example: when a patient’s blood is tested, the lab sends the results back to the clinic. This data exchange is essential for tracking a patient’s progress and ensuring that the trial’s data is complete and accurate. It ensures that data, like patient details and visit schedules, can move smoothly between systems used by different organizations.
Data Tabulation: The Study Data Tabulation Model (SDTM) defines how the data is tabled and compiled.
Example: If submitting data about adverse events, SDTM makes sure that the information, including the terms used, dates, and severity, is structured the same way for regulators to review.
Data Analysis: The analysis Dataset Model (ADaM) ensures that data is replicated and transferred for analysis.
Example: For a new drug study, ADaM helps create datasets for specific analyses, such as comparing treatment groups or assessing safety. It ensures data is ready for statisticians to do their work.
Purpose of Implementing CDISC/SDTM Standards:
- To ensure regulatory submissions to agencies such as the FDA are in standardized and compatible formats.
- To improve data quality and data sharing
- To increase efficiency and predictability through a streamlined process
- To reduce data inconsistency and overall time and cost.
Challenges and Solutions in CDISC Implementation:
- Data Mapping: This is like trying to fit puzzle pieces from different sets together. It’s tricky because old data and new standards use different ways of describing things.
Solution: Use special tools like Data Integration platforms/Data Mapping Dictionaries to make this process easier and have a clear plan.
- Changing Standards: CDISC rules are like a software update; they change over time. Keeping up with the latest versions can be hard work.
Solution: Stay informed about CDISC updates and plan for changes in advance. It’s like making sure your computer’s software is always up to date.
- Resource and Skill Gap: Imagine trying to play a sport you’ve never played before. Many organizations don’t have people who know all about CDISC.
Solution: Train your current team or hire people who already know CDISC. You can also get help from experts in CDISC.
- Cost of Implementation: Making everything CDISC-compliant can cost a lot of money. It’s like investing in a better tool for your job.
Solution: Set a onetime budget to setup CDISC standards and in the long run and volume of studies this is very cost effective in reducing the data review and submission cost. Probably in 2-3 years there may be a need to update particular section for minor changes or add new standard values. These changes will need ~40 – 60 working hrs.
- Resistance to Change: People sometimes don’t like changing the way they work. It’s like trying to convince someone to use a new phone when they love their old one.
Solution: Explain how using CDISC can make things better, like getting things done faster or making fewer mistakes. Get everyone involved and trained. And apply the Change Management process in place by identifying the change champions in the group.
Hereby concluding that CDISC standards, particularly the SDTM (Study Data Tabulation Model), stand as a critical linchpin for the collection, organization, and reporting of data in clinical trials. While their importance is undisputed, the road to CDISC implementation is not without its challenges. As we’ve explored, these challenges span from the intricacies of data mapping and legacy data conversion to the evolving landscape of CDISC standards and resource limitations. However, by taking a pragmatic approach and embracing the right strategies, organizations can successfully adapt and thrive within the CDISC framework.
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