Abstract:
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Bayesian methods that borrow strength from good prior information can lead to more efficient regulatory decision making. Many device studies have successfully leveraged control group data from historical studies to support regulatory approval. Furthermore, for medical devices, clinical data from previous generations of a device may also provide useful information. In addition, useful prior information for the investigational device may also be available from studies conducted outside the US. On account of their relative ease of implementation, methods based on the power prior approach are being increasingly used to support marketing application. Recently, dynamic borrowing methods using modified power prior methodology have also been proposed. This talk will explore different methods for leveraging prior information to support medical device regulatory submissions.
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