Monitoring Vaccine Safety by Studying Temporal Trend of Adverse Events Using VAERS

• Activity Number: 350 - Statistical Issues In Drug Development - 1
• Type: Contributed
• Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Jing Huang* and Yi Cai and Jingcheng Du and Ruosha Li and Susan S Ellenberg and Sean Hennessy and Cui Tao and Yong Chen
• Companies: University of Pennsylvania and Pieces Technology and University of Texas Health Science Center and University of Texas School of Public Health and University of Pennsylvania and University of Pennsylvania and University of Texas Health Science Center and University of Pennsylvania
• Keywords: heterogeneity; reporting rate; shrinkage estimator; signal detection; temporal trend; VAERS

Abstract

The Vaccine Adverse Event Reporting System (VAERS) plays a vital role in vaccine safety surveillance. One of the main missions of VAERS is to monitor increases in reporting rate of adverse events, as such signals can indicate safety issues caused by update of vaccines or change in vaccine practices. Existing methods can rarely be used to monitor the increase or temporal trend of reporting adverse events. In this paper, we propose a composite likelihood based variance component model to study the temporal trend of reporting adverse events using VAERS data. The proposed method is devised to identify safety signals by testing the heterogeneity of reporting proportions of adverse events across years. The proposed method accounts for the well known under-reporting of adverse events and the zero-inflation observations in passive surveillance reporting systems. In addition, our method quantifies the temporal trend of reporting adverse events over years using an Empirical Bayes estimator. We applied the proposed method to VAERS reports of trivalent influenza virus vaccine and identified 14 adverse events with significantly heterogenous reporting proportions over years.