Background: Near real-time surveillance of the influenza vaccine, which is administered to a large proportion of the US population every year, is essential to ensure safety of the vaccine. For efficient near real-time surveillance, it is key to select appropriate parameters such as monitoring start date, number of interim tests and a scheme for spending a pre-defined total alpha across the entire influenza season. Guillain-Barré Syndrome, shown to be associated with the 1976 influenza vaccine, is used to evaluate how choices of these parameters can affect whether or not a signal is detected and the time to signal. FDA has been monitoring for the risk of GBS after influenza vaccination for every influenza season since 2008.
Methods: Using Medicare administrative data and the Updating Sequential Probability Ratio Test methodology to account for claims delay, we evaluated a number of different alpha-spending plans by varying several parameters.
Results: For relative risks of 5 or greater, almost all alpha-spending plans have 100% power; however, for relative risks of 1.5 or lower, the constant and O'Brien-Fleming plans have increasingly more power. For RRs of 1.5 and greater, the Pocock plan signals earliest but would not signal at a RR of 1.25, as observed in prior influenza seasons. There were no remarkable differences across the different plans in regards to monitoring start dates defined by the number of vaccinations; reducing the number of interim tests improves performance only marginally.
Conclusions: A constant alpha-spending plan appears to be robust, in terms of power and time to detect a signal, across a range of these parameters, including alternate monitoring start dates based on either cumulative vaccinations or GBS claims observed, frequency of monitoring, hypothetical relative risks, and vaccine uptake patterns.
Keywords: Alpha-spending; Guillain-Barré Syndrome; Immunization; Influenza; Sequential test; Vaccine.
Published by Elsevier Ltd.