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Using External Evidence for Decision-Making for Medical Devices

Former Commissioner of the FDA, Dr. Scott Gottlieb, in several public presentations, would bemoan missed chances to drive innovation, and often encouraged the use of novel designs and innovative statistical methods for expediting clinical studies. He also spoke enthusiastically about looking into Bayesian methods for clinical trials to cut cost and save time. Leveraging real-world data (RWD) to improve regulatory decisions has also been a key strategic priority for the FDA [1]. A new Cytel webinar from former FDA statistician Ram Tiwari, interweaves these three themes with application to medical device trials.


Over the past several years, the FDA released Guidances on how to use real-world evidence (RWE) in regulatory decision making, including “Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices” [2]. The passage of the 21st Century Cures Act has led to the increased use of external data in the planning and regulatory submission of medical device trials. Such use of external data requires the implementation of Bayesian techniques for the construction of single arm trials, comparator arms and other innovative designs. They can also strengthen the evidence in support of a clinical trial by demonstrating the relevance of data from other completed clinical trials.

The Medical Device Innovation Consortium External Evidence Methods (MDIC EEM) Framework is intended to help stakeholders navigate the use of such methods. In a recent Cytel webinar, Dr. Ram Tiwari, Head of Statistical Methodology, Bristol Myers Squibb, presented this Framework, and then demonstrated the use of propensity scoring methods for the integration of external data in a clinical trial for medical devices.

The External Evidence Methods (EEM) Program

MDIC has assembled a Working Group comprising member organizations and other subject matter experts to guide work on this project. In this Framework, external data sources include RWD sources (e.g.: patient registries, Healthcare claims databases, Electronic Health Records), Historical clinical studies, Laboratory test data and Computational Modeling and Simulations. The purpose of the EEM Framework as per CDRH is to catalog different sources of external data and statistical methods that can be considered to leverage this data. It can be used to consider external data for premarket regulatory decision-making for medical devices. This framework also provides examples to illustrate the application of various statistical methods where external data have been leveraged.

What is the potential use of External data?

External data can be used to generate hypotheses to be tested in a prospective clinical study. It is useful in establishing Performance Goal (PG) or Objective Performance Criteria (OPC). External data can also be used as supplementary data to get additional information. And as mentioned earlier, external data is useful in trials to construct or strengthen investigational device arm and/or control arm.

Using Bayesian Methods for generating External Evidence

As uses of real-world data become more familiar for trial design and regulatory submission, sponsors might become more interested in Bayesian techniques. Some of the statistical methods that are used for generating External Evidence in the EEM Framework are:

  • Propensity score methods
  • Bayesian hierarchical models
  • Bayesian Commensurate-Prior method
  • Bayesian Power-Prior methods
  • Bayesian dynamic power-prior methods
  • Propensity score-integrated power-prior methods
  • Propensity score-integrated composite likelihood method

These methods can be used to optimize across existing datasets, design trials with lower enrollment targets, accelerate the time to regulatory submission, and strengthen the power of a trial.

In the webinar, Dr. Tiwari focuses on Propensity Score-integrated approaches used to augment clinical study using External Data and to construct control group using External Data.

Click the button to access the webinar replay.