Improved Pharmacovigilance Signal Detection Using Bayesian Generalized Linear Mixed Models

Vaccine safety monitoring is a critical component of public health given the extensive vaccination rate among the general population. However, most signal detection approaches overlook the inherently related biological nature of adverse events (AEs). We hypothesize that integrating AE field knowledge into the statistical process can facilitate and improve the accuracy of identifying vaccine-AE associations. For this purpose, we propose a Bayesian generalized linear multiple low-rank mixed model (GLMLRM) for analyzing high-dimensional post-market drug safety databases. The GLMLRM combines integration of AE ontology in the form of outcome-level groupings, low-rank matrices corresponding to these groupings to approximate the high-dimensional regression coefficient matrix, a factor analysis model to describe the dependence among responses, and a sparse coefficient matrix to capture uncertainty in both the imposed low-rank structures and user-specified groupings. An efficient Metropolis/Gamerman-within-Gibbs sampling procedure is employed to obtain posterior estimates of the regression coefficients and other model parameters, from which testing of outcome-covariate pair associations is based. The proposed approach is evaluated through simulation studies and is further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS).