Literature DB >> 22290739

A shrinkage-based comparative assessment of observed-to-expected disproportionality measures.

Geoffrey Gipson1.   

Abstract

PURPOSE: 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.
Copyright © 2012 John Wiley & Sons, Ltd.

Mesh:

Year:  2012        PMID: 22290739     DOI: 10.1002/pds.2349

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  4 in total

1.  Comparison of statistical signal detection methods within and across spontaneous reporting databases.

Authors:  Gianmario Candore; Kristina Juhlin; Katrin Manlik; Bharat Thakrar; Naashika Quarcoo; Suzie Seabroke; Antoni Wisniewski; Jim Slattery
Journal:  Drug Saf       Date:  2015-06       Impact factor: 5.606

2.  Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.

Authors:  Yauheniya Cherkas; Joshua Ide; John van Stekelenborg
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.606

3.  Effect of database profile variation on drug safety assessment: an analysis of spontaneous adverse event reports of Japanese cases.

Authors:  Kaori Nomura; Kunihiko Takahashi; Yasushi Hinomura; Genta Kawaguchi; Yasuyuki Matsushita; Hiroko Marui; Tatsuhiko Anzai; Masayuki Hashiguchi; Mayumi Mochizuki
Journal:  Drug Des Devel Ther       Date:  2015-06-12       Impact factor: 4.162

4.  Good Signal Detection Practices: Evidence from IMI PROTECT.

Authors:  Antoni F Z Wisniewski; Andrew Bate; Cedric Bousquet; Andreas Brueckner; Gianmario Candore; Kristina Juhlin; Miguel A Macia-Martinez; Katrin Manlik; Naashika Quarcoo; Suzie Seabroke; Jim Slattery; Harry Southworth; Bharat Thakrar; Phil Tregunno; Lionel Van Holle; Michael Kayser; G Niklas Norén
Journal:  Drug Saf       Date:  2016-06       Impact factor: 5.606

  4 in total

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