The development of signalling arguments can be accomplished using at least three different data strategies: intra-product qualitative methods, intra-product quantitative methods, and inter-product quantitative methods (see Table 19.2). Qualitative methods assess one or more content elements of a particular report or case series, while quantitative methods apply procedures involving numbers, proportions, or rates to groups of reports or cases. Both qualitative and quantitative methods are ultimately concerned with identifying and describing potentially associative (but not necessarily causal) relationships, but do so using different logical frameworks (Kramer and Lane, 1992).
Quantitative data strategies can be further subdivided into intra-product and inter-product procedures. Intra-product methods generate signals by examining spontaneous reports in the context of the index (monitored) product and its user population, while inter-product methods work by comparing spontaneous reporting for the index product to spontaneous reporting for another product(s) (Begaud et al., 1994a). Unlike intra-product methods, which can be either qualitative or quantitative, the inter-product methods that have been described to date are all quantitative. Intra- versus inter-product logic is a fundamental feature of spontaneous signalling method design and has been a factor throughout the history of its development (Rawlins, 1988).
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