LAUSR

Pharmacovigilance studies based on spontaneous reporting systems use disproportionality analysis methods to identify drug-event combinations with higher-than-expected reporting. Enhanced reporting is deemed as a proxy of a detected signal and is used to generate drug safety hypotheses, which can then be tested in pharmacoepidemiologic studies or randomized controlled trials. Higher-than-expected reporting means that the reporting rate of a drug-event combination of interest is disproportionately higher than the rate in a specific comparator or reference set. Currently, it is unclear which comparator is the most appropriate for use in pharmacovigilance. Moreover, it is also unclear how the selection of a comparator may affect the directionality of the various reporting and other biases. This paper reviews commonly used comparators chosen for signal detection studies (active comparator, class-exclusion comparator, and full-data reference set). We give an overview of the advantages and disadvantages of each method based on examples from the literature. We also touch upon the challenges related to the derivation of general recommendations for the selection of comparators when mining spontaneous reports for pharmacovigilance.