A Seasonality-Adjusted Sequential Test for Vaccine Safety Surveillance

Abstract

Abstract Post-market active safety monitoring is important for the timely capture of safety signals associated with exposure to a new vaccine or drug. The group sequential analysis is a common method employed in safety surveillance. Specifically, it compares the post-vaccination incidence of adverse event (AE) in a vaccinated population with a pre-specified reference level by sequentially conducting hypothesis testing during the surveillance. When the number of AEs is “too high”, a safety signal is identified. If the null hypothesis is never rejected, the vaccine is considered safe. Such an approach does not account for either the variation in determining the reference risk from a control population or the seasonality effect. Furthermore, not rejecting the null could be due to a lack of power and cannot always be interpreted as proof of safety. In this paper, we proposed a new group sequential test procedure fully accounting for both seasonality and variation from the historical controls. More importantly, we proposed to construct a confidence interval for the relative AE risk between the exposed and control groups at the end of the study, which can be used to quantify the safety of the vaccine. The proposed method is illustrated via real-data examples on anaphylaxis and examined by extensive simulation studies.