from each other, do not increase the risk of adverse events, but if they are given on the same day, there is a vaccine-vaccine interaction effect, leading to increased risk. It could also be that one or both of the vaccines, when given separately, lead to a modest excess risk of the adverse event but, when given together, lead to a much higher excess risk.


With data from VSD and separate self-controlled risk interval analyses, it was found that there was an increased risk of seizures 0 to 1 days after trivalent inactivated influenza vaccine (TIV) and also that there was an increased risk of seizures 0 to 1 days after the 13-valent pneumococcal conjugate vaccine (PCV13). To tease apart the effects from the two different vaccines and to evaluate the interaction between the two, the author of this paper suggested the approach mentioned below and worked out the formulas for the analysis. It was found that both vaccines had caused an excess risk of seizures 0 to 1 days after vaccination, irrespective of the presence of the other vaccines, and that the effects were independent of each other (Tse et al., 2012). This means that there was a positive additive interaction but no multiplicative interaction. Hence, the estimates obtained indicated that it is safer to give the two vaccines on separate days rather than on the same day.

Early-Onset Adverse Events


Both VAERS and electronic health plan data can be used to evaluate early-onset adverse events due to vaccine-vaccine interaction.


For spontaneous reporting systems, Almenoff et al. (2003) have developed a proportionality-based version of DuMouchel’s (1999) empirical Bayes multi-item gamma Poisson shrinker. Pairs of two drugs are treated as a separate unique drug, different from individual drug users. To signal a possible interaction-induced adverse event, the lower 5th percentile of the empirical base geometric mean estimate must be larger than the upper 95th percentile of the empirical base geometric mean estimate for both of the individual drugs. This approach makes sense in a data mining context, where it is necessary to have some form of formal or informal adjustment for the multiple testing.

Two other methods for evaluating interaction effects in spontaneous reports

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