Group sequential analysis  Involves repeated (periodic) analyses overtime as data accumulate, at regular or irregular interval. Compares the test statistic to a prespecified signalling threshold, and stops if the observed test statistic is more extreme than the threshold
 Commonly used in clinical trials More appropriate when data updates are less frequent Yield increased study power for a given sample size
 
Continuous sequential analysis (rapid cycle analysis)   Allows to monitor the vaccine safety problems in realtime Suitable to identify true safety signals sooner. This method can signal after single AEs, if that event occurs sufficiently early. Require updated data in a realtime or in a continuous fashion
 All data related to vaccinations and AEFIs may not be available timely for analysis (data accrual lags) The risk windows might be not fully elapsed for some AEFIs at the time of each analysis (partially elapsed window), particularly in case of influenza vaccine Inherently reduces statistical power

Signal detection method/ statistical test 
Binomialbased MaxSPRT  Based on the binomial distribution Events occurring among vaccine exposed individuals or time periods compared with the number of events among unexposed individuals to the studied vaccine/matched periods
 Best fit for selfcontrolled designs More suitable when the AEs are relatively common Account bias due to multiple looks at a data
 
Poissonbased MaxSPRT  Assumes a Poisson distribution Compare the observed number of events in a given preidentified risk period with a historical data or the scientific literature Does not depends on choice of RR, it uses a onesided composite alternative hypothesis of RR>1
 More suitable when AEFIs are very rare Minimise the risk of late detection of AEFIs due to an incorrect choice of RR Adjust for multiple looks at a data
 Relies on having accurate background rate of the outcomes for comparison Does not consider uncertainty in the estimation of expected rates, if the data are limited Limited ability to control potential confounders

Conditionalbased MaxSPRT  Assumes a Poisson process for the cumulative personātime to observe a number of AEFIs   