Incorporation of stochastic engineering models as prior information in Bayesian medical device trials

Abstract

Evaluation of medical devices 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. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters. The number of virtual patients is controlled by a discount function which uses the similarity between modeled and observed data. This method is illustrated by a case study of cardiac lead fracture. Different discount functions are used to cover a wide range of scenarios in which the type I error rates and power vary for the same number of enrolled patients. Incorporation of engineering 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 error rate and power.

Population‐specific modelling of between/within‐subject flow variability in the carotid arteries of the elderly

“…By combining vascular surface models from a real cohort of patients and a between‐subjects flow variability model, we can generate virtual chimera patients that extend the cohort beyond that which would be available with purely patient‐specific data collection. It has been acknowledged that virtual patient models must incorporate both patient variability and the model uncertainty to augment clinical trials . Our model provides one of the necessary ingredients to successful virtual trials of cerebrovascular interventions.…”

Bayesian methods in clinical trials with applications to medical devices

“…A recent article by Haddad et al (2017) as part of a project on modeling and simulation by the Medical Device Innovation Consortium uses engineering modeling for medical device performance to generate "virtual patients" that could be combined in conjunction with clinical trial data using the power prior.…”

The Use of Computational Modeling and Simulation to Create Virtual Patients: Application to Cardiac Pacing and Defibrillation Systems

“…Another distinguishing feature of medical devices is that they are often evolutionary in nature, with successive models differing only slightly from a predecessor. This enables the validation of engineering simulations on the predecessor device, providing confidence for predicting the performance of a new device.When clinical outcomes can be directly simulated in a computational model, the result can be thought of as a virtual patient [2]. If sufficiently credible, virtual patient models can inform or even replace human clinical evaluation in some applications.…”

“…When clinical outcomes can be directly simulated in a computational model, the result can be thought of as a virtual patient [2]. If sufficiently credible, virtual patient models can inform or even replace human clinical evaluation in some applications.…”

“…With a Bayesian approach, the analysis of the final study objective would include information from both the current study and the prior data. Virtual patient models can be incorporated into some studies in the same way as historical data, providing benefits of reduced trial size and duration while still controlling the study operating characteristics [2]. This practice will involve collaboration between engineers, statisticians, clinicians, and regulators.…”