Since 1997, the Clinical Data Interchange Standards Consortium (CDISC) has aimed to facilitate the accessibility, interoperability, and reusability of data to support clinical research and to ensure clinical trial data is interoperable and interpretable by organizations needing safety and efficacy data. CDISC global standards, such as the Clinical Data Acquisition Standards Harmonization (CDASH), have supported the research community in improving the efficiency, actionability, and quality of clinical trial data. The CDISC began the development of CDASH standards in 2006, and the latest version, CDASH model v1.2, was released in September 2021.
Why? The main goal of CDASH is to reduce variability in the way data is collected by standardizing this process between different studies and sponsors while providing clear traceability from data collection to submission. The CDASH global case report form (CRF) standards apply to all therapeutic areas across each clinical trial phase.
How? The CDISC guidelines help standardize legacy data stored in disparate data platforms into CDISC-compliant CRFs. CDASH provides best practices for collecting data in a user-friendly manner that maximizes data quality and feeds it smoothly into CDISC’s Study Data Tabulation Model (SDTM). Whereas CDASH guidelines help facilitate efficient data capture, the SDTM defines a standard for organizing and formatting clinical trial data in the final CRF in such a way as to facilitate their transmission, review, and reuse.
International Regulatory Harmonization
According to the US Food & Drug Administration (FDA), international harmonization of data collection standards promotes efficiency within the regulatory review process, reducing an investigational product’s time-to-market. In addition, eliminating the unnecessary duplication of trials and post-market clinical evaluations improves existing patient burdens. Lastly, standardization may help remove the need for extraneous animal testing studies without compromising drug safety and effectiveness. In 2006, the FDA required all clinical trial data to be submitted in CDISC’s SDTM. As more regulatory agencies followed suit, by 2022, CDISC had amassed more than 500 members, including major pharma companies and contract research organizations (CROs) around the globe.
Adopting standardized data guidelines is especially beneficial when conducting medical research because it facilitates efficient secondary use of accumulated clinical trial data. By making this possible, researchers can leverage a reliable, consistent set of information across different studies within the same disease indications.
CDASH in Practice
Phase I–IV studies are gold-standard methods used to assess the safety and efficacy of new drugs. In clinical practice, CDASH guidelines are implemented to make data collection straightforward and flexible enough to accommodate different capture technologies, different sites, and different study phases.
- The principal investigator (PI) conducts clinical trials and collects data on source documents according to the study protocol.
- PIs transcribe trial data onto the CRF either electronically or on paper in a format provided by the sponsor or CRO.
- The clinical data manager develops the CRF with the help of statisticians, the ClinOps team, a medical monitor, and the sponsor.
- CDASH offers guidance and best practices to develop CRFs instead of following potentially divergent standards set by individuals or teams.
Benefits of Utilizing CDASH-Compliant CRFs
CDASH standards are a subset of CDISC SDTM, which is required or recommended by several regulatory agencies worldwide. Standardized data and consistent analysis tools allow regulators to view data more accurately and identify areas of concern. Initiating and implementing the CDISC standardization path reduces costs and time needed from sponsors while ensuring trial data retains its consistency and accuracy throughout the research process. The CDASH standards reduce turnaround time for regulatory approval, helping companies expedite bringing drugs to market, which is especially critical in the development of orphan and other specialized approval pathway drugs.
CDASH Guidance & Resources for CRF Development
CDASH guidance supports the development of data collection tools which are clear, understandable, and precise. Furthermore, it helps ensure traceability of trial data from the time of collection all the way through to final analysis and regulatory submission, maintaining the integrity of source data and reinforcing a clinical trial’s findings. Some resources made available by the CDISC include the following:
- The Clinical Data Acquisition Standards Harmonization Implementation Guide (CDASHIG) establishes a standardized method of collecting data consistently across studies and sponsors while providing clear traceability of submission data and transparency to regulators and other reviewers. The CDASHIG provides examples for collecting clinical trial data and implementing the CDASH standard for CRFs
- Therapeutic Area User Guides (TAUGs) address data relevant to specific disease indications and include disease-specific metadata, informative examples, and guidance on implementing CDISC standards.
- Controlled Terminology provides code lists and valid values used with data items within CDISC-defined datasets to streamline the data collection process and make it more flexible.
Adoption and Use of CDASH Instruments
Beyond new drug applications (NDAs), Yamamoto et al. (2017) contend that CDISC SDTM can create a clinical research data repository to leverage trial data across studies within the same field of disease. Although there is recognition that connecting data generated in academia and during clinical trials can benefit all stakeholders, global standards like CDASH have had limited uptake among academic institutions. Cheng et al. (2022) postulate that if more academic clinical researchers adopt CDISC standards, it will increase the number of research datasets interoperable with industry-sponsored trials and lead to more discoveries from the secondary use of clinical data.
Collaboration between CDISC and Research Electronic Data Capture (REDCap), an electronic data capture (EDC) system available at no cost to academic, non-profit, and government institutions, has made CDASH eCRF tools readily accessible to academic researchers in the REDCap Consortium. This initiative and its ongoing development are a result of mutual demand between CDISC and REDCap Consortium members. As a test case for disease-specific CRFs, Cheng et al. (2022) developed Crohn’s Disease-specific eCRFs for dissemination to facilitate widespread data aggregation and sharing.
eSource and Standardized Clinical Research Data
In response to the growing cost burden of source data verification (SDV) in clinical trials, efforts have been made to access and transmit research data directly from electronic health records (EHRs), namely electronic source data capture (eSource). The advantages of using eSource include improving data quality for regulatory decision-making, reducing cost, maintaining data integrity, and preserving audit trails. Further, eSource contributes to efficiently using and reusing healthcare data to support clinical care delivery and research.
Rocca et al. (2019) demonstrated an approach to transmit structured data from an EHR system to a clinical trial EDC system. The research team populated electronic CRFs (eCRFs) for a Phase II clinical trial, leveraging standards from CDISC, Health Level 7 (HL7), and Integrating the Healthcare Enterprise (IHE). The CRFs included CDASH mappings which can be used by researchers working on clinical studies with standardized terms for data interoperability. Electronic Patient-Reported Outcomes (ePRO) was also included as source data capture from patients in the trial. The authors advocate for consistent and standardized systems to generate high-quality data within clinical research, registries, and quality improvement while avoiding redundancies and inconsistencies between EDC and EHR systems.
Supporting Drug Development with ePRO Standardization
According to Hudgens et al. (2023), there is a gap in the ePRO dataset structure and standardization. Currently not required to adhere to a standard model, ePRO data models often vary by Electronic Clinical Outcome Assessment (eCOA) provider and by the sponsor. The following recommendations may be implemented to aid in ePRO dataset structure and standardization to support drug development:
- Transition ePRO platforms to adopt CDISC standards
- Define data standards requirements early in the study setup and apply standards at the origin of the data
- Use standardized, clearly defined naming conventions to eliminate ambiguity and improve the traceability of ePRO data
- Implement ePRO controls and continuously monitor formatting and content for conformance
Interoperability in Health Research
The COVID-19 pandemic highlighted the need for interoperability between healthcare and research institutions to exchange scientific data. Vorisek et al. (2022) assert that despite a large amount of data in the healthcare ecosystem, data that adhered to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles were limited. Processing data across geographies, institutions, and software systems requires international standards and terminologies like the CDISC Operational Data Standard (ODM) for clinical trial data. Fast Healthcare Interoperability Resources (FHIR) is a standard used in health information technology introduced in 2011 by HL7. HL7 and CDISC have jointly released a mapping implementation guide to help transform FHIR data into CDISC CDASH (or SDTM) Implementation Guide data sets.
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