Table 3

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

Statistical approachesGeneral descriptionAdvantage/indicationChallenges/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 MaxSPRTAssumes a Poisson process for the cumulative personā€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.