FDA Announces 4 Grant Awards for Projects Exploring the Use of Real-World Data to Generate Real-World Evidence in Regulatory Decision-Making
As part of the agency’s real-world evidence (RWE) efforts, the U.S. Food and Drug Administration is announcing four grant awards (RFA-FD-20-020) to examine the use of real-world data (RWD) to generate RWE in regulatory decision-making. Through this awards program, the agency seeks to encourage innovative approaches to further explore the use of RWD while ensuring that scientific evidence supporting marketing approvals meet FDA’s high evidentiary standards.
As directed by the 21st Century Cures Act, FDA is exploring the potential use of RWD and RWE to support the approval of new drug indications or post-approval study requirements for approved drugs. In December 2018, FDA published a strategic RWE Framework in support of this goal.
The awards are:
Enhancing evidence generation by linking randomized clinical trials (RCTs) to real-world data (RWD)
This project, led by Mehdi Najafzadeh, Ph.D., M.A., M.Sc., at Brigham and Women’s Hospital and Harvard Medical School, will study the benefits of making RCTs linkable to RWD. The study aims to demonstrate how linking RCTs with RWD can enhance trials by extending patients’ follow-up time beyond trial completion, capturing additional effectiveness and safety outcomes, employing methods to minimize missing data, and generalizing RCT results to real-world target populations. Linked RCTs with RWD will also improve understanding about the underlying reasons for potential discrepancies between RCTs and non-randomized studies. The study team, which includes trialists and RWD experts in collaboration with FDA, proposes to show that linkage to RWD is a low-cost, high-yield strategy that should be adopted in future RCTs.
Applying novel statistical approaches to develop a decision framework for hybrid randomized controlled trial designs which combine internal control arms with patients' data from real-world data source
This project, led by Michael Kosorok, Ph.D., and Lisa LaVange, Ph.D., at the University of North Carolina, and Herbert Pang, Ph.D., Jiawen Zhu, Ph.D., and Gracie Lieberman, M.S., at Genentech, will explore and develop recommendations for designing studies that can reliably and rigorously combine data from different sources (e.g., an RCT and RWD) and generate evidence that could be used to support regulatory decision-making. To evaluate these study designs, the team will use simulation studies, data from completed clinical trials, and RWD sources. The team will also develop and make R-packages publicly available that can be used to evaluate specific hybrid studies. Throughout the project, the team will convene meetings of experts to share progress and obtain feedback. At the conclusion of the study, the team will generate training materials and offer to conduct trainings at designated pre-conference workshops and at FDA.
Advancing standards and methodologies to generate real-world evidence from real-world data through a neonatal pilot project
This project, led by Klaus Romero, M.D., chief science officer at the Critical Path Institute (C-Path), and Jonathan Davis, M.D., professor of pediatrics at Tufts Medical Center and U.S. academic director of the International Neonatal Consortium (INC), will support the collection of neonatal intensive care unit (NICU) data from many key stakeholders worldwide. The data will then be deposited into a Real-World Data and Analytics Platform (RW-DAP).
Although many comprehensive datasets exist based on clinical care delivered in NICUs, a lack of systematic integration, data sharing, and data standards has greatly limited neonatal drug development. In this project, data will be used to define actionable reference ranges of commonly used laboratory values in neonates. In addition, a natural history model of bronchopulmonary dysplasia (a chronic lung disease common in preterm neonates) will be created. The INC and its members will partner with C-Path’s Quantitative Medicine Program and Data Collaboration Center on this project.
The electronic medical records data collected in this project will facilitate the design and conduct of clinical trials in neonates. This collaborative effort with C-Path and INC partners will help address the fact that neonates have relatively few FDA-approved therapeutic options for various medical problems.
Transforming Real-world evidence with Unstructured and Structured data to advance Tailored therapy (TRUST)
This three-year program, led by Dan Riskin, M.D., FACS, at Verantos, seeks to understand the impact of underlying data quality on RWE study results.
As RWE is used increasingly to make clinical assertions, rigorous methodological approaches are required to maintain evidentiary standards. This study compares traditional RWE approaches (using claims or health record structured data) to more advanced RWE approaches (including deep phenotyping and data linkage) on the same patient population in a real-world clinical study. Obtaining different results when using the two approaches would suggest that advanced techniques with high-data validity could be required to achieve credible RWE results. Findings can also inform future study design and definitions of fit-for-purpose data.
The study includes innovations in deep phenotyping, data linkage, and phenotype accuracy. By studying data quality and demonstrating rigorous approaches to RWE, confidence can be increased in implementing RWE within regulatory and clinical pathways.