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Advancement for Medical Device Research Methodology

Distributed Analysis for International Research

The Center developed methodologies that have enabled the distributed analysis of international data. In this approach, a standardized data extraction is implemented and distributed to participating registries. Each registry then completes the analyses of their own registry and completely de-identified data summaries are sent back to the coordinating center. Data are then combined using multivariable hierarchical models to evaluate comparative outcomes of devices regarding the main patient-centered outcomes.


Objective Performance Criteria Development

An Objective Performance Criteria (OPC) is a numerical target value of safety or effectiveness endpoints derived from historical data from various data sources, such as clinical studies and/or registries. OPCs may be used in single-arm device evaluation that has regulatory implications. The Center is developing advanced methods to construct OPCs, combining estimates obtained from different approaches, ranging from direct analysis of registry or claims data to those reported in literature.

  • Featured projects:

    • Constructing OPC for revision after hip and knee replacement surgeries

    • OPCs for contemporary interventional treatment of occlusive disease in the superficial femoral and popliteal arteries

Machine Learning and Artificial Intelligence

The center is leveraging machine learning and artificial intelligence to facilitate the evaluation of patient outcomes and predictors in the context of medical device. Machine learning methods have the advantage of taking into account complex interactions between predictors and help identify patient populations among whom the treatment works best. These information help clinicians understand population-specific treatment effect better and assist with clinical decision making.

  • Featured projects:

    • Predictors of reoperation following sling procedures

    • Predictors of mortality after liver cancer ablation and surgical resection


Advanced Study Design and Analytical Support

The Center uses its expertise in epidemiology, health services research, biostatistics, and regulatory science to help investigators construct studies to answer important clinical and regulatory questions pertinent to medical devices. The Center offers advanced study design and analytical guidance and support (e.g. propensity score, inverse probability weighting, classification trees, and other methods) to help investigators navigate around complex device questions.

  • Frameworks:

    • IDEAL-D: a rational framework for evaluating and regulating the use of medical devices.

    • A framework for evidence evaluation and methodological issues in implantable device studies

  • Featured projects:

    • Using CART to predict readmission risk after colorectal cancer surgery

    • Using advanced methods to identify volume threshold for intact abdominal aortic aneurysm repair

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