Overview of Program:

This program aims to establish a more predictable pathway for use of external evidence methods (EEM), such as new, innovative (Frequentist/Bayesian) methods and the cataloging of existing methods for evidence fusion from data external to a clinical trial.  External trial data includes but is not limited to real-world data (RWD), real-world evidence (RWE), engineering modeling and simulation, similar device clinical trial data, to support regulatory medical device decisions and other stakeholder decisions.

Program Background:

Availability and demands for medical device evidence have grown exponentially; addressing patient safety, therapeutic efficacy, and benefit-risk determinations. Evidence acquisition to meet these demands are well-documented drivers of medical device clinical trial size, complexity, timelines, and costs. At the same time, and in contrast with traditional, prospective evidence acquisition, patient and provider demands for faster treatment access, and innovative treatment options are increasing. Given the legitimacy of these stakeholders’ evidence demands, combined with the realities of resource (e.g. budget and time) limitations, traditional evidence acquisition techniques focused on prospective data collection will soon no longer meet patient and provider needs.  One solution is to leverage evidence generated externally to the prospective question being investigated.  Such external evidence, along with methods of integration, might leverage computer modeling and simulation, historical clinical trial, publication, and/or real-world evidence.

Current as of January 21, 2021
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