Literature DB >> 19358225

Quantitative signal detection using spontaneous ADR reporting.

A Bate1, S J W Evans.   

Abstract

Quantitative methods are increasingly used to analyse spontaneous reports. We describe the core concepts behind the most common methods, the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM). We discuss the role of Bayesian shrinkage in screening spontaneous reports, the importance of changes over time in screening the properties of the measures. Additionally we discuss three major areas of controversy and ongoing research: stratification, method evaluation and implementation. Finally we give some suggestions as to where emerging research is likely to lead.

Mesh:

Year:  2009        PMID: 19358225     DOI: 10.1002/pds.1742

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


  215 in total

1.  Identifying adverse events of vaccines using a Bayesian method of medically guided information sharing.

Authors:  Colin John Crooks; David Prieto-Merino; Stephen J W Evans
Journal:  Drug Saf       Date:  2012-01-01       Impact factor: 5.606

2.  Terminological challenges in safety surveillance.

Authors:  Andrew Bate; Elliot G Brown; Stephen A Goldman; Manfred Hauben
Journal:  Drug Saf       Date:  2012-01-01       Impact factor: 5.606

3.  Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study.

Authors:  Véronique Pizzoglio; Ismaïl Ahmed; Pascal Auriche; Pascale Tuber-Bitter; Françoise Haramburu; Carmen Kreft-Jaïs; Ghada Miremont-Salamé
Journal:  Eur J Clin Pharmacol       Date:  2011-12-06       Impact factor: 2.953

4.  Vaccine-based subgroup analysis in VigiBase: effect on sensitivity in paediatric signal detection.

Authors:  Sandra de Bie; Katia M C Verhamme; Sabine M J M Straus; Bruno H Ch Stricker; Miriam C J M Sturkenboom
Journal:  Drug Saf       Date:  2012-04-01       Impact factor: 5.606

5.  A signal detection method to detect adverse drug reactions using a parametric time-to-event model in simulated cohort data.

Authors:  Victoria R Cornelius; Odile Sauzet; Stephen J W Evans
Journal:  Drug Saf       Date:  2012-07-01       Impact factor: 5.606

6.  Biclustering of adverse drug events in the FDA's spontaneous reporting system.

Authors:  R Harpaz; H Perez; H S Chase; R Rabadan; G Hripcsak; C Friedman
Journal:  Clin Pharmacol Ther       Date:  2010-12-29       Impact factor: 6.875

7.  Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System.

Authors:  Elisabetta Poluzzi; Emanuel Raschi; Domenico Motola; Ugo Moretti; Fabrizio De Ponti
Journal:  Drug Saf       Date:  2010-04-01       Impact factor: 5.606

8.  An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance.

Authors:  Xiaofeng Zhou; Sundaresan Murugesan; Harshvinder Bhullar; Qing Liu; Bing Cai; Chuck Wentworth; Andrew Bate
Journal:  Drug Saf       Date:  2013-02       Impact factor: 5.606

9.  Signalling paediatric side effects using an ensemble of simple study designs.

Authors:  Jenna M Reps; Jonathan M Garibaldi; Uwe Aickelin; Daniele Soria; Jack E Gibson; Richard B Hubbard
Journal:  Drug Saf       Date:  2014-03       Impact factor: 5.606

10.  tcTKB: an integrated cardiovascular toxicity knowledge base for targeted cancer drugs.

Authors:  Rong Xu; QuanQiu Wang
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05
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