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Activity Number: 77 - Complex Designs and Composite Endpoints of Medical Device Clinical Studies and Benefit-Risk Analysis of Diagnostic Tests
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #301835 Presentation
Title: Survival Analysis of Hierarchical Learning Curves in Assessment of Cardiac Device and Procedural Safety
Author(s): Usha Govindarajulu* and Sandeep Bedi and Aaron Kluger and Frederic Resnic
Companies: SUNY Downstate Medical Center and SUNY Downstate and Baylor University and Lahey Hospital and Medical Center
Keywords: cardiac device; Cox model; hierarchical; learning curve; simulations; survival analysis

Many people rely on cardiac surgical procedures and devices to treat or manage their cardiovascular diseases. The failure of these devices/procedures could have grave consequences. One reason cardiac devices tended to fail was due to physician error; there is a learning effect for the physician or operator to come up to speed in skillfully implanting devices and conducting procedures. To better understand these learning effects, we had previously modeled the effects in simulations in a hierarchical setting with physicians clustered within institutions using our unique methodology, where we had employed these in both hierarchical linear modeling and GEE models. In this setting, we have demonstrated how to apply similar methodology revised in a survival analytic framework or time-to-event analyses. Through simulations and real dataset applications, we found that, as seen before, modeling the learning rate can be dataset specific and one shape may be better. The goal of this research is to model cardiac device and procedure learning curve effects in a time-to-event setting so that this knowledge may allow for the improvement of both short and long-term patient survival.

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

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