title: A shrinkage-based comparative assessment of observed-to-expected disproportionality measures.
abstract: OBJECTIVE
  Disproportionality analysis is an important tool for interpreting spontaneous adverse event reports in pharmacovigilance. There exist a variety of disproportionality measures (DPMs) for use in safety signaling; however, it is not always clear which method is best suited for a particular need. A framework for comparing the various DPMs is necessary to fully understand the consequences of quantitative signal detection implementation decisions. Here, the mathematical relationship between these measures is explored through a comparison of the underlying equations and a shrinkage approach is adopted to further clarify these relationships.
  
  
  METHODS
  Many DPMs take the form of a ratio of the number of observed (O) cases and the number of expected (E) cases (i.e., O/E). Because O is unchanged by the method selected, the method-specific E (E(DPM) ) is the fundamental difference between the values produced by these DPMs. Clarification of the relationship between these DPMs is pursued through the use of a shrinkage parameter (s).
  
  
  RESULTS
  It is demonstrated that any arbitrary DPM, which can be defined as O/E(DPM) , can also be expressed as a function of the reporting odds ratio (ROR) and s. This common equation allows for a straightforward comparison of the varying methods and the ability to clearly characterize the approaches according to their relative signal detection performance irrespective of the specific dataset to which the methods are applied. A novel DPM, the independent reporting ratio (IRR), provides an example of how the described framework can improve our understanding of disproportionality analyses and lead to the development of new methods.
  
  
  CONCLUSIONS
  Explicitly defining DPMs as RORs with applied shrinkage provides a convenient method for understanding their relative signal detection performance and insight into the relative contributions to DPM shrinkage.
authors.affiliation: Patient Safety, AstraZeneca Pharmaceuticals LP, Wilmington, DE, USA. Geoffrey.Gipson@astrazeneca.com
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Gipson
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D015233:Models, Statistical; D060735:Pharmacovigilance