The FDA employs the use of advisory committees in the process of approving and evaluating medical devices. The Center for Devices and Radiological Health (CDRH) currently has established 22 advisory committees to assist in providing “independent, professional expertise and technical assistance on the development, safety and effectiveness, and regulation of medical devices and electronic products that produce radiation,” according to the FDA website. Many of the outside experts that sit on each of these medical device committees have financial ties to the companies that sponsor the devices under review. Thus, this research project focused on examining how financial ties influence the device approval process.
Currently, the FDA has defined three classes of devices: Class I, which have minimal potential for harm; Class II, which are what the bulk of medical devices are classified as; and Class III devices, which usually sustain or support life. Class II devices must undergo the Premarket Notification (PMN) or 510(k) process, whereas class II devices must undergo submit a Premarket Approval (PMA) application. Such devices are typically discussed and voted on during each advisory committee’s meetings, and the documents (including transcripts, rosters, and conflict waivers) from the meetings are archived on an FDA database. When/if a member sitting on the committee has a conflict of interest with a sponsor of a device to be discussed, he/she will usually receive a waiver and the conflict is announced in the transcript. As such, it is possible to track which committee members (voting and nonvoting) have conflicts of interest, and what types of conflicts (financial or otherwise) the member(s) have.
Throughout this research experience, I had the opportunity to learn about the importance of background reading, how to collect data and organize massive amounts of information, the process of translating said data into Excel sheets, and finally, how to clean and get summaries of the collected data via the use of Stata. The process of learning how to code in Stata was extremely rewarding, and taught me a lot about patience and the importance of the iterative nature of research.