– Principal Pharmacoepidemiologist, Eli Lilly and Company , Fishers, Indiana, United States
Background: Spontaneous reporting systems of adverse drug events are important source for postmarking evaluation and monitoring of adverse drug events. However, these sources have limitations.
Objectives: To describe common sources of bias in signal detection and clarification utilizing spontaneous reporting systems.
Methods: Literature review and application of disproportionality analyses to the Food and Drug Administration Adverse Event Reporting System (FAERS) was used to prepare best-practices guiding researchers on appropriate application of methods and interpretations of findings from spontaneous reporting systems.
Results: Pharmacovigilance analysis of spontaneous reporting data is encountered with a myriad of challenges that stem from the characteristics of data source which bias the interpretation of identified signals. The following sources of bias are described with real-world examples: confounding; masking effect; Weber effect; ripple effect; multiplicity effect; polytherapy bias; notoriety bias; stimulated reporting bias; reporting bias; misclassification and diagnostic bias; and limitations of disproportionality analysis methods.
Conclusions: Despite the challenges of passive safety surveillance, spontaneous reporting of adverse drug events is a crucial source for drug safety management. Identified signals should be interpreted in light of the inherent limitations of these systems, and sources of bias that affect signal interpretation should be considered.
