The GVP Module P.I: Vaccines for prophylaxis against infectious diseases describes issues to be considered when applying methods for disproportionality analyses for vaccines, including the choice of the comparator group and the use of stratification. Effects of stratification on data mining in the US Vaccine Adverse Event Reporting System (VAERS) (Drug Saf 2008;31(8):667-74) demonstrates that stratification can reveal and reduce confounding and unmask some vaccine-event pairs not found by crude analyses. However, Stratification for Spontaneous Report Databases (Drug Saf 2008;31(11):1049-52) highlights that extensive use of stratification in signal detection algorithms should be avoided. Vaccine-Based Subgroup Analysis in VigiBase: Effect on Sensitivity in Paediatric Signal Detection (Drug Saf 2012;35(4)335-346) further examines the effects of subgroup analyses based on the relative distribution of vaccine/non-vaccine reports in paediatric ADR data.
Four techniques are compared in Comparing data mining methods on the VAERS database (Pharmacoepidemiol Drug Saf 2005; 14(9):601-9: empirical Bayes geometric mean (EBGM), lower-bound of the EBGM's 90% confidence interval (EB05), proportional reporting ratio (PRR), and screened PRR (SPRR) and concludes the value of each method varies according to the situation.
The article Adverse events associated with pandemic influenza vaccines: comparison of the results of a follow-up study with those coming from spontaneous reporting (Vaccine 2011;29(3):519-22) reported different patterns of reactions observed with two methods compared for first characterisation of the post-marketing safety profile of a new vaccine, which may impact on signal detection.
|Annex 1.||Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes|