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.  

This framework is in alignment with other MDIC’s initiatives, including the National Evaluation System for health Technology Coordinating Center’s (NESTcc’s) mission of accelerating the timely, reliable, and cost-effective development of RWE to enhance regulatory and clinical decision-making. The EEM Framework expands the repertoire of MDIC resources that focuses on reliable and cost-effective Real-World Evidence (RWE) throughout the medical device total product lifecycle NESTcc’sResearch Methods Framework and Data Quality Framework as well as the Real-World Clinical Evidence Generation: Advancing Regulatory Science and Patient Access for In Vitro Diagnostics (IVDs) Framework.

Purpose of the EEM Framework

This framework is intended to help stakeholders navigate their way through leveraging external data by:

1. Cataloging different sources of external data
2. Cataloging statistical methods that can be considered to leverage external data
3. Considering uses of external data, when appropriate, for regulatory decision-making for medical devices
4. Providing examples to illustrate the application of various statistical methods where external data have been leveraged

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