Modern implantable medical devices have brought improved quality of life to many patients. Evaluation via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. Incorporation of these models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length while still controlling type I and type II error rates. This paper presents a straightforward method for augmenting a clinical trial using virtual patient data, where the number of virtual patients is based on the similarity between modeled and observed data. The use of this method is illustrated by a case study based on a model for cardiac lead fracture.