Computer-aided Clinical Trials for Medical Devices Robustness - Evaluation

Computer modeling and simulation for medical device evaluation has been pursued as a means for reducing the cost and scope of clinical trials while minimizing the risk of unforeseen adverse outcomes. Advances in computational technologies and algorithms have further enabled analysis with complex and realistic Bayesian models to be applied to clinical trials. However, a major challenge is quantifying the degree of uncertainty in simulation outcomes as well as clearly communicating simulation assumptions and parameters such that the results can be considered as regulatory-grade evidence. 

In this work, we formulate the Computer-Aided Clinical Trial (CACT) within a Bayesian statistical framework allowing explicit modeling of assumptions and utilization of simulation at all stages of a clinical trial. To quantify the robustness of the simulated endpoints of a CACT with respect to an assumption, we derive δ-robustness as the minimum perturbation of the base prior distribution before the outcome of the CACT changes. The CACT framework and estimation of δ-robustness is applied to the Rhythm ID Goes Head-to-head Trial (RIGHT) which is a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. We demonstrate the utility of the framework and how the results of δ-robustness evaluation can be utilized at various stages of a clinical trial.