In this chapter we have concentrated on the use of the BCPNN, partly because it is the most examined system used at present. There has, however, been one study (van Puijenbroek et al., 2002) comparing the BCPNN with other methods. As mentioned above, in various centres, different measures are used to quantify the extent to which a certain ADR is reported in a disproportionate relationship to a certain drug compared with the generality of the database, i.e. standing out from the background of all reports. In this comparative study the level of concordance was measured of the various estimates to the measures of the IC and IC-2std produced from the BCPNN.
The investigation was performed on the data set of the Netherlands Pharmacovigilance Foundation (Lareb), which maintains the spontaneous ADR reporting system in the Netherlands on behalf of the Dutch Medicines Evaluation Board.
In essence all the other methods could pick up the signals which the BCPNN could, and indeed more with a lower number of cases. When the "disproportionality" was based on relationships with four or more reports (about 11% of the Lareb database), all the methods were comparable. It was only at low count values where any difference could be detected.
The above finding is significant. The precise method used for data mining should be based upon the benefits and drawbacks of each. Crucial to the Bayesian method is the initial setting of the a priori probability. How this is set determines the performance of the BCPNN at low counter values. At the UMC we chose an a priori probability of independence that is consistent with the WHO definition of a signal and the previous publication (Edwards et al., 1990), suggesting that normally more than one report would be needed to trigger an expert to think that he/she had found a signal, unless there was something exceptional qualitatively about a report (such as a case with proven, true re-challenge). Moreover, the WHO database has many more incident reports than the Lareb data base so that as greater numbers of reports are submitted, little time will be lost in finding the signal even though the BCPNN requires about three more to trigger.
It is clear that the other methods may be just as suitable as the BCPNN for routine use to identify
Table 22.1. Conditions, advantages and disadvantages of different measures of disproportionality.
Measure of disproportionality
Expected 'null value'
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