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Activity Number: 676 - Analysis and Reporting: Benefit-Risk and Robust Models
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #327229 Presentation
Title: Real Data Applications of Learning Curves in Cardiac Devices and Procedures
Author(s): Usha Govindarajulu* and David Goldfarb and Frederic Resnic
Companies: SUNY Downstate School of Public Health and Montfiore Medical Center and Lahey Clinic
Keywords: learning curve; GEE; procedure; cardiac device; hierarchical; mediation

In the use of medical device procedures, learning effects have been shown to have a significant impact on the outcome, and are a critical component of medical device safety surveillance. To support estimation of these effects, we evaluated our methods for modeling these rates within several different actual datasets representing patients treated by physicians clustered within institutions to show the flexibility of this method across applications. We employed our unique modeling for the learning curves to incorporate the hierarchy between institution and physicians, and then modeled them within established methods that work with hierarchical data. Within the actual datasets, we looked at two device types and also two procedure types. We also tried mediation analyses within the GEE framework for these various devices/procedures as well. We found that the choice of shape used to produce the "learning-free" dataset would still be dataset-specific and log or power series tended to work best. We were able to show the flexibility of applying our method in different data applications. This can, therefore, be used across the board for device and procedure safety.

Authors who are presenting talks have a * after their name.

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