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Background

Non-randomized studies: Studies with historical controls and the use of Objective Performance Criteria (OPCs) Jeff Cerkvenik Statistics Manager Medtronic, Inc. Background.

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Background

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  1. Non-randomized studies: Studies with historical controls and the use of Objective Performance Criteria (OPCs) Jeff CerkvenikStatistics ManagerMedtronic, Inc.

  2. Background • “If clinical data are needed, FDA and industry should consider alternatives to randomized, controlled clinical trials when potential bias associated with alternative controls can be addressed.” • Source: “The Least Burdensome Provisions of the FDA Modernization Act of 1997: Concept and Principles; Final Guidance for FDA and Industry”, Oct. 4, 2002, CDRH ODE and CBER.

  3. Randomized or Non-randomized Considerations • Randomization helps minimize bias. • But, what if patients can’t be blinded? • Consider amount of bias (e.g., death vs. QOL). • Can evaluators be blinded? • Is a control group necessary for a reasonable evaluation? • Magnitude of change from existing predicate device(s).

  4. Types of Non-randomized Trials • Trials with historical controls • Trials using objective performance criteria (OPC) • Registry trials

  5. Definition • Objective Performance Criteria (OPC) are performance criteria based on broad sets of data from historical databases (e.g., literature or registries) that are generally recognized as acceptable values. These criteria may be used for surrogate or clinical endpoints in demonstrating the safety or effectiveness of a device. • Source: “The Least Burdensome Provisions of the FDA Modernization Act of 1997: Concept and Principles; Final Guidance for FDA and Industry”, Oct. 4, 2002, CDRH ODE and CBER.

  6. Historical control? • Are historical data available? • Are the populations comparable? • For the primary endpoint, not necessarily demographics.

  7. OPC • How to determine OPC? • Past, similar, approved devices. • Use the point estimate or that study’s OPC? • Statistician’s job? • How do we balance statistical need / sample size / reasonable OPC? • E.g., if we expect the failure rate to be 5%, should OPC be 10% (n=239) or 15% (n=76)?

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