Many Americans rely on cardiac surgical procedures and devices such as pacemakers and thrombolytic catheters to treat or manage their cardiovascular diseases. However, the failure of these cardiac devices and 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. In order to better understand these learning effects, we had previously modeled the resulting learning curve effects in simulations a hierarchical setting with physicians clustered within institutions using our unique methodology (see the work of Govindarajulu et al 2017). Previously, we had employed these in hierarchical linear modeling and also in generalized estimating equations. In this setting, we have demonstrated how to apply similar methodology but revised in a survival analytic framework or time-to-event analyses. Through simulations and real dataset applications, we found that, out of the three shapes modeled to fit the learning curve, the logarithmic shape tended to have the best fit, similar to previous work (see the work of Govindarajulu et al 2017). However, as seen before, modeling the learning rate can be dataset specific and one shape may be better than another. We learned that modeling the learning rate could also be applied in the survival analysis setting through this new methodology. The goal of this paper 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.
Keywords: Cox model; cardiac device; hierarchical; learning curve; simulations; survival analysis.
© 2018 John Wiley & Sons, Ltd.