Unlocking the Potential of Real-World Evidence (RWE) in Regulatory Submissions

Introduction: The Rising Role of RWE in Regulatory Filings

Real-world evidence (RWE) has rapidly transitioned from a supportive tool to a cornerstone of regulatory submissions, spurred by initiatives like the 21st Century Cures Act. This shift reflects a growing recognition of real-world data's (RWD) ability to provide critical insights into treatment effectiveness and safety beyond controlled clinical trial settings. However, navigating the complexities of RWE integration demands adherence to stringent evidentiary requirements and strategic planning.

In this post, we’ll explore key best practices for leveraging RWE in regulatory filings, drawing insights from guidance documents to help set you up for success.

Why RWE Matters for Regulatory Submissions

RWE offers unique opportunities for:

1. Characterizing Disease Burden and Natural History: RWE provides comprehensive data on disease progression and patient outcomes, essential for understanding unmet need.
2. Supporting Label Expansions: As seen in several regulatory approvals, RWE has served as substantial evidence for expanded drug indications.
3. When Randomization is Unfeasible: For rare diseases or conditions with no standard of care, RWE enables studies when randomization is impractical or unethical.

Best Practices for Integrating RWE into Regulatory Filings

1. Early Engagement with Regulatory Bodies

• • Why it Matters: Early alignment with the FDA or other regulatory bodies ensures study design, data sources, and methods meet regulatory expectations. This gives opportunities to integrate regulatory feedback early and align on content, thereby improving approval prospects of the RWE.
  • Key Actions:
    • Schedule meetings with regulators, such as Type B or C, early in the planning phase.
    • Share initial study design concepts, data sources, and protocols for feedback.
    • Iterate study designs and protocol based on regulatory input to address potential gaps.
    • Ensure continued communication throughout the study to accommodate any emerging regulatory feedback and guidance.

     2. Selecting Fit-for-Purpose Data

• • Why it Matters: The strength of RWE depends heavily on the quality and suitability of the data used.
  • Key Actions:
    • Conduct a thorough feasibility analysis to evaluate all potential data sources.
    • Choose data that is both relevant to the research question and adheres to regulatory standards (e.g., electronic health records [EHRs], claims, or registries).
    • Validate key variables (e.g., exposure, outcome, covariates) to ensure they are accurately measured and defined.
    • Document the rationale for data source selection, including feasibility assessments for all data sources considered.

     3. Robust Study Design

• • Why it Matters: A well-designed study minimizes bias, ensures reproducibility, and strengthens the evidence generated for regulatory approval.
  • Key Actions:
    • Predefine study protocols and SAPs: Specify analyses and gating criteria before data collection to prevent or selective reporting.
    • For externally controlled trials (ECTs), ensure the real-world arm reflects trial-like conditions with well-matched baseline characteristics.

     4. Transparent and Objective Endpoints

• • Why it Matters: The choice of endpoints plays a critical role in shaping a study’s reliability.
  • Key Actions:
    • Use validated, objective endpoints wherever possible (e.g., tumor response rate over progression-free survival in oncology single-arm trials).
    • Ensure alignment with regulatory expectations for endpoint selection during early engagements.
    • Conduct independent reviews of outcome measures, such as radiographic assessments, to mitigate investigator bias.

     5. Mitigating Bias

• • Why it Matters: Bias can affect the validity of findings and may result in regulatory concerns.
  • Key Actions:
    • Proactively manage potential biases like immortal time bias or selection bias, particularly in non-randomized settings.
    • Address confounding through careful selection of variables and robust statistical techniques, such as inverse probability weighting or propensity score matching.
    • Use sensitivity analyses to test the robustness of results.
    • Consider quantitative bias assessments (QBA) to quantify residual bias.

     6. Ensuring Data Reliability

• • Why it Matters: Reliable data enhances confidence in the study’s findings and supports regulatory approval.
  • Key Actions:
    • Ensure data completeness and accuracy through rigorous quality checks.
    • Establish clear provenance and traceability of all data sources.
    • Maintain inspection-readiness with thorough documentation of all study-related activities.

     7. Transformation to CDISC¹ Formats

• • Why it Matters: The FDA requires all submitted data, including real-world data (RWD), to follow standardized formats that ensure consistency, transparency, and ease of review. Proper formatting enables efficient analysis, replication of findings, and strengthens the credibility of the submission while avoiding delays or compliance issues.
  • Key Actions:
    • Transform RW data to meet regulatory data standards, such as ^[1] formats.
    • Provide complete documentation for the transformed RW data, including data dictionaries, and study data reviewer’s guides.
    • Maintain traceability from raw data to final results, ensuring transparency in any modifications or exclusions.

     8. Audit Readiness

• • Why it Matters: Regulatory agencies may require on-site inspections or detailed reviews of submitted data.
  • Key Actions:
    • Maintain all source data in accessible formats for regulatory review.
    • Provide transparent audit trails that detail data modifications and transformations.
    • Retain logs of research er qualifications and activities related to the study.

Conclusion: Navigating the Future of RWE

The increasing reliance on RWE in regulatory decision-making presents both opportunities and challenges. Sponsors who embrace early engagement, rigorous study design, and data transparency position themselves for success. By adhering to these best practices, organizations can not only meet evidentiary requirements but also contribute to advancing patient care through robust and actionable evidence.

As the landscape evolves, ongoing collaboration between industry and regulators will be essential to unlock the full potential of RWE in shaping the future of medicine.

Call to Action

Ready to harness the power of RWE in your next regulatory ? With deep regulatory experience and a proven track record of supporting sponsors, Landmark is here to guide you through every step of the process - from data curation and study design to ensuring audit readiness and compliance with regulatory standards.  Share your thoughts or let us know how we can support your journey toward impactful, real-world insights!

Learn more about best RWD best practices in our recently published White Paper here.

[1] Clinical Data Interchange Standards Consortium (CDISC).