|
Abstract:
|
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.
|
