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The conditional power prior is a popular method to borrow information from a single prior data source. The amount of borrowing is controlled by the power parameter which is fixed before running the new study. However, fixing this parameter before running a new study is often difficult and may be unwise because if the outcomes in the current study are much different from the prior data outcomes, the power parameter cannot be changed to reflect a more appropriate degree of borrowing. On the other hand, treating the power parameter as a random variable to be updated via Bayes theorem may relinquish control over how much to borrow in cases where regulatory oversight recommends a conservative approach.Previous authors have determined the power parameter at the end of the current study based on "stochastic" similarity in the outcomes between the current study and the prior data. In this paper, we introduce some modifications to those methods. First, we determine the power parameter based on similarity between a percentage of the current study outcome data available at an interim look and the prior outcome data. This may limit potential for operational bias resulting from the determination of the power parameter after the current study is complete. Next, we introduce a new measure of similarity between the current (interim) and prior data that limits similarity by a pre-specified clinical margin. The proposed clinical similarity region may be readily understood by clinicians who need to assess when such borrowing is clinically appropriate. Through simulations, we show that our approach has low bias and good power, while reducing type I error rate in areas outside of the "similarity region". An example of a hypothetical medical device study illustrates its potential use in practice.

Signal detection methods have been used extensively in post-market surveillance to identify elevated risks of adverse events. However, these statistical methods have not been widely used in detecting AE signals for medical devices. In this paper, we focused on the use of a likelihood ratio test (LRT)-based method in identifying adverse event (AE) signals associated with left ventricular assist devices (LVADs) using Medical Device Reporting (MDR) data. Among 110,927 adverse event entries identified in MDR data for LVADs, the LRT method detected 18 AE signals which included seven bleeding-related AEs such as hemolysis, thrombosis, hematuria, thrombus, blood loss, and hemorrhage. The LRT method was also applied to longitudinal data from 2007 to 2019 where a monotone alpha-spending function was used to ensure the control of type I error at each look and overall for trend analysis. Furthermore, the LRT method was compared to proportional reporting ratios (PRRs), Bayesian confidence propagation neural network (BCPNN), and simplified Bayes methods and found to be the most conservative method when examining the total number of detected signals, given its ability to control type-I error and the false discovery rate.

Effective post-market safety surveillance of medical devices is critical for public health. However, many current statistical methods for safety signal detection do not control for type I error when assessing multiple device and adverse event (AE) combinations. This can result in increased false signals, underscoring the need for more robust statistical methods. Moreover, the duration of medical device use can be an important factor to consider in safety surveillance. In this study, we adapted a likelihood ratio test (LRT) based method, which was initially developed and applied to drugs, to identify safety signals for left ventricular assist devices (LVAD). Among patients with chronic, advanced left ventricular failure, we analyzed AE data for HeartWare and HeartMate II patients during a two-year period and further incorporated person-years (henceforth exposure-time). The novel modified LRT and conventional Z-test with