Table 3

Sequential statistical approaches for postlicensure vaccine safety surveillance (description, indication and challenges)

Statistical approaches	General description	Advantage/indication	Challenges/weakness
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	Does not allow to capture the safety problems as soon as possible Very complex to compute Limited ability to control potential confounders
Continuous sequential analysis (rapid cycle analysis)	Allows examination of data frequently (as often as desired) over time. Surveillance starts as soon as uptake of the vaccine starts or delayed until a pre-set number of events occur	Allows to monitor the vaccine safety problems in real-time 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 real-time 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
Binomial-based 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 self-controlled designs More suitable when the AEs are relatively common Account bias due to multiple looks at a data	Limited ability to control potential confounders
Poisson-based 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 one-sided 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
Conditional-based MaxSPRT	Assumes a Poisson process for the cumulative person‐time to observe a number of AEFIs	Accounts for uncertainty in historical data Adjust for multiple looks at a data	Assumes constant event rates are in historical and surveillance data Limited ability to control potential confounders

AE, adverse event; AEFI, adverse events following immunisation; MaxSPRT, maximised sequential probability ratio test; RR, relative risk.