Yale University-Mayo Clinic CERSI

Center of Excellence in Regulatory Science and Innovation

The Yale-Mayo CERSI conducts high-quality, high-impact collaborative research to support several areas of focus in the FDA strategic plan for regulatory science. Research topic areas include: adoption/de-adoption of FDA-approved medical products, postmarket surveillance, development and application of novel analytics, and patient-centered regulatory decision-making.

Current Projects

Characterizing use, safety and efficacy of brand-name and generic drugs used to treat hypothyroidism

Generic drugs are approved based on bioequivalence to the brand name agents. However, there are sometimes concerns among patients and clinicians that generic and brand name drugs are not equivalent and have differing effects. Using a large administrative claims data source that includes information on privately insured and Medicare Advantage enrollees of all ages, we will characterize patterns of use of generic and brand-name L-thyroxine products and then compare the effectiveness and safety of generic and brand-name L-thyroxine among both new users and recent switchers.


Linking data sources to elucidate non-fatal and fatal opioid-related overdose epidemiology and the role of FDA-regulated products

Overdose, or the syndrome where a drug causes loss of consciousness/coma, is the leading cause of accidental death in the U.S. It is clear that medications- not just illegal drugs- are increasingly involved in overdoses. As such, the FDA has pledged to re-examine the role of medications, particularly opioids and benzodiazepines, in the dramatic increase in overdoses. One of the main challenges in this work is that while we may know who has experienced an overdose, it has been very difficult to assess what medications they may have had access to or what they were taking. The primary reason for this difficulty is that there are many different sources of data on medication receipt and it is challenging to link these datasets. In this project, we will overcome those challenges, linking a variety of datasets that will generate a more accurate assessment of which medications at which doses put patients at risk for- or protect them from- overdose.