A Bayesian framework for safety signal detection from medical device data.

Abstract

Safety evaluation is important during both the pre-market clinical trials and post-market surveillance. In either a pre-market or post-market setting wherein the safety of a device is compared to that of a control device, it is desirable to identify any difference in the safety between two devices as expeditiously as possible. Here, we introduce the Bayesian hierarchical framework for the safety assessment in two-arm clinical trials, with signal detection accomplished by evaluating the effect size of each adverse event (AE) measured by odds ratio or relative risk. The framework starts with a standard hierarchical Bayesian model with a parametric distribution as a common prior for the effect sizes of all AEs. Then, it is extended with a non-parametric prior, Dirichlet Process Prior, to allow for more flexibility. After that, to account for the rare events in some trials, it is further extended with the option of additional zero-inflated parameters and calculation of regularized effect size. Extra incorporation of exposure-time information is available under each framework. The performance of the proposed technique, along with its extensions, is studied by simulation. The application of the proposed Bayesian framework is demonstrated by data from a two-device clinical trial, the newer left ventricular assist device (LVAD) and the existing LVAD. The Bayesian analysis result is then compared to a traditional frequentist technique. Through both simulation and application, the proposed Bayesian technique is shown to be robust to the selection of priors of the variance component, and has comparative and under some scenarios even better performance than the frequentist technique. Overall, the developed Bayesian framework is a feasible alternative to the frequentist method for safety evaluation of medical device clinical trials.