Medical cyber-physical systems, such as the implantable cardioverter defibrillator (ICD), require evaluation of safety and efficacy in the context of a patient population in a clinical trial. Advances in computer modeling and simulation allow for generation of a simulated cohort or virtual cohort which mimics a patient population and can be used as a source of prior information. A major obstacle to acceptance of simulation results as a source of prior information is the lack of a framework for explicitly modeling sources of uncertainty in simulation results and quantifying the effect on trial outcomes. 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 results at all stages of a clinical trial. To quantify the robustness of the CACT outcome with respect to a simulation assumption, we define δ-robustness as the minimum perturbation of the base prior distribution resulting in a change of the CACT outcome and provide a method to estimate the δ-robustness. 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 through an application to the Rhythm ID Goes Head-to-head Trial (RIGHT), which was a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. Finally, we introduce a hardware interface that allows for direct interaction with the physical device in order to validate and confirm the results of a CACT for implantable cardiac devices.