A pragmatic approach for dynamically incorporating predicate device data in prospective diagnostic test studies.

Clinical studies are generally required to characterize the accuracy of new diagnostic tests. In some cases, historical data are available from a predicate device, which is directly relevant to the new test. If this data can be appropriately incorporated into the new test study design, there is an opportunity to reduce the sample size and trial duration for the new test. One approach to achieve this is the Bayesian power prior method, which allows for the historical information to be down-weighted via a power parameter. We propose a dynamic method to calculate the power parameter based on first comparing the data between the historical and new data sources using a one-sided comparison, and second mapping the comparison probability through a scaled-Weibull discount function to tune the effective sample size borrowed. This pragmatic and conservative approach is embedded in an adaptive trial framework allowing for the trial to stop early for success. An example is presented for a new test developed to detect Methicillin-resistant