Literature DB >> 25005708

Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance.

G Niklas Norén1, Ola Caster, Kristina Juhlin, Marie Lindquist.   

Abstract

Pharmacovigilance seeks to detect and describe adverse drug reactions early. Ideally, we would like to see objective evidence that a chosen signal detection approach can be expected to be effective. The development and evaluation of evidence-based methods require benchmarks for signal detection performance, and recent years have seen unprecedented efforts to build such reference sets. Here, we argue that evaluation should be made against emerging and not established adverse drug reactions, and we present real-world examples that illustrate the relevance of this to pharmacovigilance methods development for both individual case reports and longitudinal health records. The establishment of broader reference sets of emerging safety signals must be made a top priority to achieve more effective pharmacovigilance methods development and evaluation.

Mesh:

Year:  2014        PMID: 25005708     DOI: 10.1007/s40264-014-0198-z

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  19 in total

1.  A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database.

Authors:  M Lindquist; M Ståhl; A Bate; I R Edwards; R H Meyboom
Journal:  Drug Saf       Date:  2000-12       Impact factor: 5.606

2.  A decade of data mining and still counting.

Authors:  Manfred Hauben; G Niklas Norén
Journal:  Drug Saf       Date:  2010-07-01       Impact factor: 5.606

Review 3.  Quantitative signal detection using spontaneous ADR reporting.

Authors:  A Bate; S J W Evans
Journal:  Pharmacoepidemiol Drug Saf       Date:  2009-06       Impact factor: 2.890

4.  Data-driven prediction of drug effects and interactions.

Authors:  Nicholas P Tatonetti; Patrick P Ye; Roxana Daneshjou; Russ B Altman
Journal:  Sci Transl Med       Date:  2012-03-14       Impact factor: 17.956

5.  Web-scale pharmacovigilance: listening to signals from the crowd.

Authors:  Ryen W White; Nicholas P Tatonetti; Nigam H Shah; Russ B Altman; Eric Horvitz
Journal:  J Am Med Inform Assoc       Date:  2013-03-06       Impact factor: 4.497

Review 6.  Defining a reference set to support methodological research in drug safety.

Authors:  Patrick B Ryan; Martijn J Schuemie; Emily Welebob; Jon Duke; Sarah Valentine; Abraham G Hartzema
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

7.  Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system.

Authors:  R Harpaz; W DuMouchel; P LePendu; A Bauer-Mehren; P Ryan; N H Shah
Journal:  Clin Pharmacol Ther       Date:  2013-02-11       Impact factor: 6.875

8.  A reference standard for evaluation of methods for drug safety signal detection using electronic healthcare record databases.

Authors:  Preciosa M Coloma; Paul Avillach; Francesco Salvo; Martijn J Schuemie; Carmen Ferrajolo; Antoine Pariente; Annie Fourrier-Réglat; Mariam Molokhia; Vaishali Patadia; Johan van der Lei; Miriam Sturkenboom; Gianluca Trifirò
Journal:  Drug Saf       Date:  2013-01       Impact factor: 5.606

9.  Digital drug safety surveillance: monitoring pharmaceutical products in twitter.

Authors:  Clark C Freifeld; John S Brownstein; Christopher M Menone; Wenjie Bao; Ross Filice; Taha Kass-Hout; Nabarun Dasgupta
Journal:  Drug Saf       Date:  2014-05       Impact factor: 5.606

10.  Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest.

Authors:  Richard D Boyce; Patrick B Ryan; G Niklas Norén; Martijn J Schuemie; Christian Reich; Jon Duke; Nicholas P Tatonetti; Gianluca Trifirò; Rave Harpaz; J Marc Overhage; Abraham G Hartzema; Mark Khayter; Erica A Voss; Christophe G Lambert; Vojtech Huser; Michel Dumontier
Journal:  Drug Saf       Date:  2014-08       Impact factor: 5.606

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  25 in total

1.  A method for systematic discovery of adverse drug events from clinical notes.

Authors:  Guan Wang; Kenneth Jung; Rainer Winnenburg; Nigam H Shah
Journal:  J Am Med Inform Assoc       Date:  2015-07-31       Impact factor: 4.497

2.  Performance of Stratified and Subgrouped Disproportionality Analyses in Spontaneous Databases.

Authors:  Suzie Seabroke; Gianmario Candore; Kristina Juhlin; Naashika Quarcoo; Antoni Wisniewski; Ramin Arani; Jeffery Painter; Philip Tregunno; G Niklas Norén; Jim Slattery
Journal:  Drug Saf       Date:  2016-04       Impact factor: 5.606

3.  How Confident Are We about Observational Findings in Healthcare: A Benchmark Study.

Authors:  Martijn J Schuemie; M Soledad Cepeda; Marc A Suchard; Jianxiao Yang; Yuxi Tian; Alejandro Schuler; Patrick B Ryan; David Madigan; George Hripcsak
Journal:  Harv Data Sci Rev       Date:  2020-01-31

4.  Authors' reply to Hennessy and Leonard's comment on "Desideratum for evidence-based epidemiology".

Authors:  J Marc Overhage; Patrick B Ryan; Martijn J Schuemie; Paul E Stang
Journal:  Drug Saf       Date:  2015-01       Impact factor: 5.606

5.  Comment on: "Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance".

Authors:  Rave Harpaz; William DuMouchel; Nigam H Shah
Journal:  Drug Saf       Date:  2015-01       Impact factor: 5.606

6.  Authors' reply to Harpaz et al. comment on: "Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance".

Authors:  G Niklas Norén; Ola Caster; Kristina Juhlin; Marie Lindquist
Journal:  Drug Saf       Date:  2015-01       Impact factor: 5.606

7.  Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection.

Authors:  Xiruo Ding; Trevor Cohen
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

8.  Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Authors:  Justin Mower; Devika Subramanian; Trevor Cohen
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

9.  Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data.

Authors:  Martijn J Schuemie; George Hripcsak; Patrick B Ryan; David Madigan; Marc A Suchard
Journal:  Proc Natl Acad Sci U S A       Date:  2018-03-13       Impact factor: 11.205

Review 10.  Electronic Health Data for Postmarket Surveillance: A Vision Not Realized.

Authors:  Thomas J Moore; Curt D Furberg
Journal:  Drug Saf       Date:  2015-07       Impact factor: 5.606

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