Learning curve estimation in medical devices and procedures: hierarchical modeling. (3rd May 2017)

• Record Type: Journal Article
• Authors: Govindarajulu, Usha S.; Stillo, Marco; Goldfarb, David; Matheny, Michael E.; Resnic, Frederic S.
• Is Part Of: Statistics in medicine. Volume 36:Number 17(2017)
• Page Start: 2764
• Page End: 2785
• Publication Date: 2017-05-03
• Subjects: learning curve -- GEE -- GLME -- simulations -- medical device -- hierarchical; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7309
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In the use of medical device procedures, learning effects have been shown to be a critical component of medical device safety surveillance. To support their estimation of these effects, 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. We found that both methods performed well, but that the GEE may have some advantages over the generalized linear mixed effect models 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 as employed various smoothing methods to the plots. We concluded that while both hierarchical methods can be used with our mathematical modeling of the learning curve, the GEE tended to perform better across multiple simulated scenarios in order to accurately model the learning effect as a function of physician and hospital hierarchical data in the use of a novel medical device. We found that the choice of shape used to produce the 'learning‐free' dataset would be dataset specific, while the choice of smoothing method was negligibly different from one another. This was an important application to understand how best to fit this unique learning curve function for hierarchical physician and hospital data. Copyright © 2017 John Wiley & Sons, Ltd.