Statistical reasoning and algorithms in signal detection 

To be able to outline at least two ways in which statistical reasoning and algorithms can bring value to pharmacovigilance

To be able to explain the idea of disproportionality

To be able to compute the expected number of reports and the relative reporting ratio (RRR)

To be able to compute the proportional reporting ratio (PRR) and explain how it differs from the relative reporting ratio (RRR)

To be able to compute the reporting odds ratio (ROR)

To be able to explain the issue of random variability for disproportionality analysis, and its main consequence

To be able to compute the Information Component (IC), and explain its relationship to the relative reporting ratio (RR)

To be able to interpret IC values and uncertainty intervals, and to explain the purpose and effect of statistical shrinkage in IC values

To be able to understand that each disproportionality measure can be used in practice with several different decision rules, and explain the purpose of these decision rules

To be able to recall that the proportional reporting ratio (PRR) and the reporting odds ratio (ROR) don't use shrinkage, and the practical consequence of this

To be able to understand that disproportionality analysis is not the only way that statistical reasoning and algorithms can be useful in signal detection

To be able to recall two specific examples of strength-of-evidence aspects other than disproportionality analysis

To be able to recall that statistical reasoning can benefit everyone whereas statistical algorithms bring value in settings with more reports than can be manually reviewed and surveyed

To be able to recall that disproportionality analysis is based on a very simple model and that its results must be interpreted with caution