CRDSA’s Work Groups serve the data-sharing ecosystem by addressing the barriers and logistical hurdles to contributing and using shared clinical research data. Several overarching principles guide our Work Group development. First, we’re active partners and collaborators, always looking for ways we can support and amplify the work of other organizations. Second, we believe transformative change comes from supporting big forward-thinking objectives with a series of actionable and achievable deliverables. Third, we recognize that process and technical solutions often require advancements in regulatory and organizational policies to be fully realized.
We currently have four active Work Groups supported by over 50 member Subject Matter Experts:
Innovative Trial Design
Develops new approaches to trial design enhanced by data reuse and drives policy change to facilitate innovative trial execution
Supplemental Control Demonstration Project: Supplementing RCTs with appropriately sourced Clinical Trial and Real-World Data has the potential to address critical research and trial execution challenges. Our Supplemental Controls Demonstration Project in Non-Small Cell Lung Cancer builds on FDA work by CRDSA member organizations. Topics addressed include data provenance, study and subject selection methodologies, and multi-modal analysis exploring the synergistic reuse of Clinical Trial Data and RWD.
Objective: Address gaps in current guidance and develop consensus standards to inform Health Authority guidance for the regulatory use of patient-level Clinical Trial and Real-World Data.
Enabling Platform Trial Data Sharing: Many platform trials do not have a plan for how data will be shared by and between the platform trial participating organizations. Data sharing outside of the platform trial is generally considered out of scope. Concurrent and non-concurrent control platform trial design adds timing (when) and access (who) complexity.
Objective: Key stakeholder alignment around how data generated in platform trials are shared and used for secondary use.
Secondary Use Standards
Establishes common standards and best practices to ensure the integrity, quality, and usability of contributed data
Secondary Use Data Contributions: Common standards for sharing data intended for secondary data use can create process efficiency and information transparency that would benefit the research community and, equally important, benefit data contributors by ensuring their investment in data preparation time and resources maximizes research outcomes. The work group’s report on CRDSA’s survey of biopharma and academic researchers, “Establishing a Basis for Secondary Use Standards in Clinical Trials,” was published in March 2023.
Objective: Data contribution standards responsive to end-user research needs, including datasets and documentation, transformation transparency, and metadata provision.
Secondary Use Research: Today there are no recognized standards for analyzing individual patient-level data that can be employed by researchers, applied by journal editors and peer reviewers to assess submitted papers, and used by other researchers to evaluate the validity of analyses and conclusions drawn. This creates a risk for data contributors, researchers, and publications that inadvertent errors may be made, potentially leading to conclusions that are not robust and may therefore be detrimental to patient care.
Objective: Develop standards for researchers using secondary IPD to ensure analysis validity and integrity of results.
Advances data privacy and governance policies
Data Protection Policy Decision Guide: The evolving global data protection landscape makes it difficult for many organizations to navigate policy development for internal data reuse and external sharing. This initiative, targeted at senior leaders, highlights the key issues decision-makers need to consider to develop and implement a data protection policy that balances research access and utility with organization-appropriate risk tolerance.
Objective: Help organizations develop data policies that promote broad access and maintain high research utility while accounting for organization-appropriate risk tolerance.
Information Loss Framework: This initiative is developing a methodology to measure information loss due to secondary use processing. Employing a standardized model for measuring post-processing retained data fidelity will allow data contributors and researchers to understand the degree of information loss and help quantify dataset value.
Objective: A standardized framework for measuring post-processing retained accuracy.
Technology and Innovation
Explores new technologies to support novel data governance approaches and advance data sharing principles
Data Sharing Technology Assessment Framework: The Technology and Innovation team has developed a framework to assist people responsible for internal and external data sharing platforms with unbiased vendor-neutral information on the relative maturity, best-fit use cases, and potential benefits/challenges of candidate technologies. The framework was released in November 2022 and a supporting R Shiny application is in development.
Objective: Create a framework to evaluate how and when emerging technologies can help data sharing initiatives and platforms develop new governance approaches that advance patient privacy, enhance data utility, improve infrastructure security, and facilitate broader access to shared data.