Clinical research projects are evolving with strict compliance to standards set by respected organisations to ensure public health and safety. One is CDISC standards that contract research organizations (CROs) and clinical trial facilities follow. Any clinical development services company incorporating CDISC standards in their work would likely use these to ensure that the data collected in clinical trials are high quality. Results can be easily shared and analyzed with other researchers and regulatory agencies.
What Is CDISC
Clinical Data Interchange Standards Consortium (CDISC), a global not-for-profit organisation, creates data standards for clinical research. This organisation works with the Food and Drug Administration (FDA) and other global healthcare agencies to support the data acquisition, submission, exchange, and storage for biopharmaceutical and medical product development, including medicines, medical devices, and therapies.
CDISC sets guidelines for electronic clinical research forms (ECRFs) used in clinical trials. The ECRF design consists of controlled terminologies, questions, and dynamic behaviours like conditional logic, derivations, and edit checks. CROs use the right tools for collecting data to save time and money in building electronic data capture (EDC) or a system that collects clinical research data in digitised or electronic form.
Benefits Of CDISC Standards
Regulatory agencies, such as the US FDA and Japan’s Pharmaceutical and Medical Devices Agency, require CROs to adopt global data standards developed by CDISC. CDISC standards simplify clinical research data collection from patient-submitted questionnaires and laboratory sampling data.
Below are the benefits of following CDISC standards in clinical research:
1. Streamlined Data Management
More streamlined data management allows clinical researchers to study results quickly and share them with the community. Excellent data management is crucial in clinical trials, especially in vaccine development.
2. Fast Review Process
For pharmaceutical companies, CDISC standards promote faster data submission and review processes as required by FDA and other global healthcare agencies.
3. Better Clinical Research Solutions
Technology vendors follow CDISC standards when creating solutions that the medical research community needs using standardised datasets.
Types Of CDISC Standards
CDISC standards are presented in user guides, data models, and implementation guides. These are globally recognised and followed by healthcare and biopharmaceutical companies.
The following CDISC standards allow clinical research sponsors and CROs to submit clinical trial data in electronic format to regulatory agencies:
SDTM outlines human clinical trial and non-clinical study data tabulations, which are part of the FDA product application and approval process. It sets instructions on how to organise and format data to streamline data collection, analysis, management, and reporting.
This CDISC standard is built around patient observations described in domains and variables with corresponding datasets and tables. Most observations gathered during the clinical study are divided into general observation classes, which include interventions, events, and findings.
SDTM standards are continually changing, so CDISC releases new versions frequently.
This CDISC standard describes the best practices when creating clinical research forms, and it is highly recommended by the FDA and other regulatory bodies. CDASH sets a standard data collection process across clinical research studies for more transparent traceability between data gathering and submission until regulatory agency review.
CDASH generates human-readable data optimised for data capture, data quality, and investigator site activities. SDTM focuses on the data structure for seamless reporting and analysis, whereas the CDASH framework focuses on gathering questions to determine the clinical trial’s true intention and make it easier for clinicians to understand the data.
The FDA highly recommends that sponsors and CROs design clinical trials utilising CDISC-defined data elements, as it helps create SDTM domains quickly. Using CDASH and SDTM together can better serve the clinical research study team.
This CDISC standard describes how to develop analysis datasets and related metadata. ADaM guides statistical programmers to create tables, listings, and figures easily. ADaM promotes clear communication and traceability when analysing SDTM datasets. This way, reviewers can evaluate and approve clinical trial submissions more quickly.
ADaM and SDTM are directly related to each other. Grouping SDTM datasets improves visual clinical trial review, whereas arranging ADaM variables leads to easier result calculation. Researchers must gather and utilise variables from SDTM sources and arrange them into different groups to obtain result.
ADaM has standard data structures, which include the Analysis Dataset Subject Level, Basic Data Structure, and Occurrence Data Structure. These datasets adhere to the ADaM fundamental principles and conventions. There are specific requirements for creating ADaM datasets, including the study protocol, CRF, SDTM datasets, mapping specifications, and mockup shells.
Conclusion
Clinical research is a critical aspect of the healthcare sector. Implementing the CDISC standards can help promote clinical research transparency from the protocol stage to data analysis and reporting. Furthermore, it reduces timelines and costs by hastening regulatory processes for quicker marketing authorisation.
