Safety Data Mining

GSK receives approximately 90,000–100,000 spontaneous adverse event reports each year. Recognizing that its researchers needed new tools to help them understand and prioritize the most important data, in 2002 GSK began using data mining to evaluate its safety data.

Because postmarket information is reported voluntarily, there is no control group (i.e., it is impossible to know how many people took a drug, how many people experienced an event, and how many people experienced that event after taking the drug). Therefore, it may be difficult to evaluate precisely how rare or common a particular adverse event is. For example, if there are 30 reports of strokes occurring in individuals taking drug X, is that too many? This is a difficult question to answer as it depends on how common strokes are in the normal population, how much exposure there has been to drug X, and other factors. Answering such questions therefore requires an objective, systematic approach.

GSK uses a statistical approach called disproportionality analysis (DPA) to identify rare events that occur at a greater frequency than would be expected by chance. The DPA calculation is derived from a two-by-two table such as that shown in Figure 8-1. If the ratio of A/(A + B) is greater than the ratio of C/(C + D), there is a potential association between the drug and the event of interest.

For example, to determine whether there was an association between drug X and stroke, one would look at the number of stroke cases reported for drug X as a proportion of all the adverse events reported for that drug. One would then compare that result with the number of strokes reported for all drugs as a proportion of all the adverse events reported for all

FIGURE 8-1 Disproportionality analysis calculation.

SOURCE: Almenoff, 2007.

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