|
Abstract:
|
With the rapid development of computing technology, Bayesian statistics have increasingly gained more attention in various areas of public health. However, the full potential of Bayesian sequential methods applied to vaccine adverse events surveillance has not yet been realized, despite the acknowledged practical benefits and philosophical advantages using the Bayesian approach. In this talk, we describe how sequential analysis can be performed under the Bayesian paradigm in the field of vaccine safety. We compare the performance of the frequentist sequential method, specifically, Maximized Sequential Probability Test Ratio (MaxSPRT), and a Bayesian sequential method through both simulations and a real world vaccine safety example. The performance is evaluated using three measurements: false positive rate, false negative rate, and average earliest time to signal. Depending on the background rate of adverse events, the Bayesian sequential method could significantly improve the false negative rate and decrease the earliest time to signal. We consider the proposed Bayesian sequential approach to be a preferred alternative for vaccine safety surveillance.
|
