Spontaneous reporting (SR) adverse event system databases, large observational databases, large clinical trials, and large health records databases comprise repositories of information that may be useful for early detection of potential harms associated with drugs, devices, and vaccines. All of the data sources include many different adverse events and many medical products, so that any approach designed to detect "important" signals of potential harm must have adequate specificity to protect against false alarms yet provide satisfactory sensitivity for detecting issues that really need further investigation. Algorithms for evaluating potential risks using information from these sources, especially SR databases, have been described in the literature. The algorithms may seek to identify potential product-event associations without any prior specifications, to identify events associated with a particular product or set of products, or to identify products associated with a particular event or set of events. This article provides recommendations for using information from postmarketing spontaneous adverse event reporting databases to provide insight into risks of potential harm expressed by safety signals and offers guidance regarding appropriate methods, both frequentist and Bayesian, to use in various situations as a function of the objective of the analysis.
Keywords: Bayes; empirical Bayes; pharmacovigilance; propensity; risk identification; score.