Article: Incorporation of stochastic engineering models as prior information in Bayesian medical device trials
Varied stakeholder data requirements from medical device clinical trials have grown substantially and continue to rise. This growth raises concerns that the clinical evidentiary requirements for successfully bringing innovative medical devices to market are unsustainable. To address this growth in clinical data requirements, the MDIC working group engaged in the development and implementation of a Virtual Patient model using engineering models to simulate virtual patient outcomes. With a focus on properly informing clinical evaluation, the incorporation of virtual patient outcomes is controlled by a discount function which uses the similarity between modeled and observed data. This paper demonstrate that the virtual patient technique’s incorporation of engineering models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length, while controlling type I error rate and power.
Case Studies in Successful Implementation of Effective Risk-Based Monitoring in Medical Device Companies
The MDIC Clinical Trial Innovation and Reform Steering Committee highlights cases studies from three medical device companies on effective implementation of risk-based monitoring practices: Case Studies in Successful Implementation of Effective Risk-Based Monitoring in Medical Device Companies
A Framework for Simplification of Clinical Trials in Regulated Medical Devices
The MDIC Clinical Trial Innovation and Reform Steering Committee outlines their vision for clinical trial innovation in this white paper: A Framework for Simplification of Clinical Trials in Regulated Medical Devices
Data Leaning in Clinical Studies
Members of the MDIC Clinical Trial Innovation and Reform Steering Committee and Clinical Trial Design working group offer a series of case studies on efforts to streamline the amount of data collected in clinical trials: Data Leaning in Clinical Studies
Data Collection in Medical Device Clinical Trials
In 2014, MDIC conducted a survey of member medical device companies to understand how much data was collected in medical device clinical trials, and examine the extent to which that data was applicable in the respective regulatory submission. The results indicate there is opportunity to improve the efficiency of clinical trials by reexamining how much data is collected in clinical trials, and reimagining our approach to identifying the data set required for a successful clinical trial. See the full survey results report here.
MDIC Medical Device Supplement to CTTI Critical to Quality Factors Principles Document
In 2015, the Clinical Trials Transformation Initiative’s Quality by Design Project published their CTTI Quality by Design Project – Critical to Quality (CTQ) Factors Principles document intended to “support proactive, cross-functional discussions and decision making at the time of trial development about 1) what aspects of a trial are critical to generating reliable data and providing appropriate protection of research participants (“critical to quality” [CTQ] factors) and 2) what strategies and actions will effectively and efficiently support quality in these critical areas.”
MDIC supports CTTI’s approach to identifying the factors that matter in clinical trials in order to generate reliable data. In addition to CTTI’s comprehensive set of recommendations, the MDIC Clinical Trial Design working group has created a supplement with some additional considerations for quality and efficient medical device clinical trials. This supplement should be used in conjunction with CTTI’s recommendations.