In a recent article in Statistics in Medicine on July 30, Dr. Usha Govindarajulu, a biostatistican, and her colleagues refined their previous methodology, to also model the probability of successful implementation of surgical devices and procedures in a survival analytic framework or time‐to‐event analyses. To do this, she and her team studied learning curve effects of practitioners, which are a critical component of medical device and procedural surveillance. Learning curve effects were estimated by their unique modeling for modeling success rates within a complex, simulated dataset representing patients treated by physicians clustered within institutions. They then further evaluated the learning curves using established statistical methods for survival analysis with hierarchical data. The goal of this paper was 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.
Read the abstract here.