Activity Number:
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624
- Trial Design and Analysis Issues in Medical Devices
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Type:
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Contributed
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Date/Time:
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Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Medical Devices and Diagnostics
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Abstract #324500
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View Presentation
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Title:
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Learning Curve Estimation in Medical Devices and Procedures: Hierarchical Modeling
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Author(s):
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Usha Govindarajulu* and Michael Matheny and David Goldfarb and Marco Stillo and Frederic Resnic
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Companies:
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SUNY Downstate and Vanderbilt University and Montefiore Medical Center and SUNY Downstate Medical Center and Lahey Clinic
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Keywords:
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learning curve ;
GEE ;
GLME ;
simulations ;
medical device ;
hierarchical
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Abstract:
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In the use of medical device procedures, learning effects have been shown to be a critical component of device safety surveillance. To support their estimation , we evaluated multiple methods for modeling these rates within a complex simulated dataset representing patients treated by physicians clustered within institutions. We employed unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians, and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE) and generalized linear mixed effect models (GLME). We found that both methods performed well, but that the GEE may have some advantages over the GLME for ease of modeling and a substantially lower rate of model convergence failures. We then focused more on using GEE and performed a separate simulation to vary the shape of the learning curve as well employ various smoothing methods to the plots. This was an important application to understand how best to fit this unique learning curve function for hierarchical physician and hospital data.
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Authors who are presenting talks have a * after their name.