Group sequential method for observational data by using generalized estimating equations: Application to Vaccine Safety Datalink

Abstract

Post-market medical product surveillance is important for detecting rare adverse events that are not identified during preapproval. The goal of surveillance is to assess over time for elevated rates of adverse events for new medical products. These studies utilize administrative databases from multiple large health plans. We propose a group sequential method using a permutation approach with generalized estimating equations to account for confounding. A simulation study is conducted to evaluate the performance of the group sequential generalized estimating equation method compared with two other approaches. The methods are then applied to a vaccine safety application from the Vaccine Safety Datalink.