FDA Collaborations
The Center collaborates with the Food and Drug administration through our Fellowship Program and through jointly developed research collaborations with our faculty and students. A few of these research collaborations are featured below.
Generating Reproducible Real-World Evidence with Multi-Source Data to Capture Unstructured Clinical Endpoints for Chronic Disease
This project aims to develop statistical approaches to enable use of real-world clinical data to define treatment responses in large and heterogenous patient populations and inform product labeling for specific indications and patient groups.
FDA’s Advancing Real-World Evidence Program
Visit this page for more information on the FDA’s real-world evidence program including goals of the program, processes for participation, and general information.
This page hold information about recipients of the FDA 2023 Grant Awards for RWE generation, including Harvard-MIT CRS’ project on novel processes for generating RWE from electronic health records. Visit the page to learn more about the project, as well as other recipients of the 2023 Grant Awards.
Safety Events Associated with Endovascular Aneurysm Repair Devices
Using real-world data derived from electronic healthcare records, this project applies advanced informatics and statistical learning pipelines to assess the association of endovascular aneurysm repair devices with rare and long-term safety outcomes.
Read the accompanying editorial and viewpoint.
Additional information on the endovascular device safety can be found on the FDA website.
Detecting Postmarket Safety Signals for Medical Devices
This project aims to employ automated tools and methodologies for detection and evaluation of safety issues for medical devices in large volume pre- and post-market data sources.
Assessing Targets on the Pediatric Molecular Target List Under the RACE Act
Under the newly enacted RACE Act, the FDA is authorized to require pediatric studies for oncology drugs developed for adult populations if the drug targets a molecular target relevant to a pediatric cancer.
This project aims to apply methods in natural language processing to identify emerging biomarkers that may be relevant to pediatric cancers, and to further characterize the scientific evidence underlying inclusion or exclusion of molecular targets on the Pediatric Molecular Target List. The project also involves a systematic review of pediatric oncology trial and drug development activities to establish benchmarks for assessing the impact of the RACE Act.
Find Relevant Publications for the Project:
Safety Signal Discernment and Biostatistics (SANEST) for Paclitaxel
This project aims to provide recommendations on the analyses and necessary regulatory actions for the integration of randomized controlled trials (RCT) and real world data (RWD) for patients treated with paclitaxel-coated devices, including drug-coated balloons and drug-eluting stents.
The work investigates the impact of misclassified devices on paclitaxel-coated device data as well as the limitations of data integration. Outputs include the development of guidelines for misclassification methods, data source quality assessment, and data integration methods.