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    2021 Duke Margolis 2-Day Meeting: Preliminary results from ongoing trial replication efforts

    Our summary

    This two day meeting focused on “Evaluating RWE from Observational Studies in Regulatory Decision-Making: Lessons Learned from Trial Replication Analyses”. Preliminary results from ongoing observational trials designed to replicate the results of randomized clinical trials were presented.

    The strengths and limitations of observational studies were discussed in the context of evaluating a drug product’s effectiveness for regulatory decision-making. While some of the studies successfully duplicated results based on prespecified metrics, showing that supplication is possible with the fit for purpose study designs and data, others did not.

    This meeting contemplated the lessons learned from the work and potential next steps to keep advancing our understanding of how and when RWE may support regulatory decisions. An appropriate study design aimed to address an explicit question is critical and the TARGET trial approach for observational studies was provided as a good starting point and framework for reliable observational studies.

    Throughout the two days, panelists discussed opportunities and challenges of using RWE and highlighting ways to make RWE more credible:

    • FDA urged the audience to consider the regulators’ perspective, which is based on the current evidentiary standards for effectiveness. Regulators require sufficient information to understand both whether a drug works and the magnitude of treatment effect so that clinicians can make decisions between available treatments.
    • FDA will issue several RWE guidance documents in 2021 (register for our RWE Regulatory Intel newsletter for updates on this).
    • FDA and panelists suggested that RWE Investigators/Sponsors build credibility in the interpretation of RWE by: using the TARGET trial framework when designing an observational study; pre-specifying primary, secondary and sensitivity analyses; providing quantitative assessments of bias and estimates of uncertainty.

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