Résumé
Pharmacovigilance aims at detecting adverse effects of marketed drugs. It is generally based on a Spontaneous Reporting System (SRS) that consists of the spontaneous reporting, by health professionals, of events that are supposed to be adverse effects of marketed drugs. SRS supply huge databases, the human-based exploitation of which cannot be exhaustive. Automated signal generation methods have been proposed in the literature but no consensus exists concerning their efficiency and applicability due to the difficulties in evaluating the methods on real data.The objective is to propose SRS modelling in order to simulate realistic data sets that would permit completion of the methods’ evaluation and comparison. In fact, as the status of the drug-event relationships is known in the simulated data sets, generated signals can be labelled as “true” or “false.”The spontaneous reporting is viewed as a Poisson process depending on: the drug’s exposure frequency, the delay from the drug’s launch, the adverse events’ background incidence and seriousness, and the reporting probability. This reporting probability, quantitatively unknown, is derived from the qualitative knowledge found in the literature and expressed by experts. This knowledge is represented and exploited by means of a set of fuzzy rules.Then, we show that the SRS modelling permits to evaluate the automatic signal generation methods proposed within pharmacovigilance and contribute to generate a consensus on drugs’ postmarketing surveillance strategies.