MDIC’s External Evidence Methods (EEM) program aims to assist stakeholders with use of EEM, such as new, innovative, and existing methods for evidence fusion from data external to a clinical study. The purpose of incorporating external data is often to create efficiency in medical product development and regulatory decision-making, thereby bringing new, safe, and effective technologies to market sooner to help patients in need. External data may also provide insights into the clinical performance of the diagnostic device being studied. External data can potentially be used in regulatory decision-making throughout the total product life cycle (TPLC).
MDIC EEM Framework is a document intended to help stakeholders navigate their way through the nuts and bolts of leveraging external data. Informed by a number of public forums and a survey of medical device manufacturers, the document catalogs different sources of external data and some of the traditional and novel statistical methods (Frequentist and Bayesian) applicable to the design and analysis of a clinical study in which external data play a role. It also provides references to actual past studies leveraging external data in which some of these statistical methods were successfully applied to support the approval/clearance of medical devices, or the modification of their indications. In all these examples, the external data being leveraged are subject-level data. Most methods cataloged in this document rely on subject-level external data for their implementation.