In this paper we aim to answer the question, "How can modeling and simulation of physiological systems be used to evaluate life-critical implantable medical devices?" Clinical trials for medical devices are becoming increasingly inefficient as they take several years to conduct, at very high cost and suffer from high rates of failure. For example, the Rhythm ID Goes Head-to-head Trial (RIGHT) sought to evaluate the performance of two arrhythmia discriminator algorithms for implantable cardioverter defibrillators, Vitality 2 vs. Medtronic, in terms of time-to-first inappropriate therapy, but concluded with results contrary to the initial hypothesis- after 5 years, 2,000+ patients and at considerble ethical and monetary cost. In this paper, we describe the design and performance of a Computer-aided Clinical Trial (CACT) for Implantable Cardiac Devices where previous trial information, real patient data and closed-loop device models are effectively used to evaluate the trial with high confidence. We formulate the CACT in the context of RIGHT using a Bayesian statistical framework. We define a hierarchical model of the virtual cohort generated from a physiological model which captures the uncertainty in the parameters and allow for the systematic incorporation of information available at the design of the trial. With this formulation, the estimates the inappropriate therapy rate of Vitality 2 compared to Medtronic as 33.22% vs 15.62% $(\mathrm{p}\lt 0.001)$, which is comparable to the original trial. Finally, we relate the outcomes of the computer- aided clinical trial to the primary endpoint of RIGHT.