gamma-Poisson shrinker” (MGPS) is described in DuMouchel (1999), DuMouchel and colleagues (2001), O’Neill and Szarfman (2001), Szarfman and colleagues (2002), and Fram and colleagues (2003). The latter system is in use at the FDA and several pharmaceutical manufacturers. These strategies have proven to be effective ways to reduce the noisiness of the disproportionality measures, but the role of such analyses in drug safety signaling is still evolving. Regulators and manufacturers are beginning to adopt them as an additional tool to help prioritize their pharmacovigilance efforts, but no one is proposing that they can provide definitive inferences without extensive follow-up.
There are several differences between drugs and devices that tend to change the potential use of spontaneous report databases. On the side of making reports regarding devices more useful, the more transparent action of many devices compared to a drug can often make even a single report very informative. For example, if a faulty device delivers an electric shock to the patient, physical inspection of the device might lead immediately to a suggested corrective design of the device—no reliance on statistical analysis is needed.
But there are other features of the device world that make it harder to use spontaneous report databases. There are more devices than drugs, and manufacturers modify the design of their devices much more frequently than drugs get reformulated. This means that a particular version of a typical device has a smaller user base than a typical drug, and thus a typically smaller number of reports in a database.
In addition, adverse event coding is more complicated for devices than for drugs, and not yet as standardized. A typical device adverse event report includes information both about what happened to the device as well as what happened to the patient, whereas the drug reports contain only the second sort of information. In the drug world, the MedDRA coding system has been adopted by the majority of collectors of adverse event reports around the world; no such standardization has been accomplished in the device world.
A device adverse event database might have several hundred thousand reports, with just a paragraph of narrative text describing what went wrong. A data mining approach would need to group the adverse event descriptions into at most a few thousand adverse event types, and preferably do this automatically, without the need for human review of each report individually. A computer analysis can try to form clusters of reports based on the common occurrence of words or phrases in the narrative. The automated discovery of which words or phrases are indicative of important