Literature DB >> 17538548

Novel statistical tools for monitoring the safety of marketed drugs.

J S Almenoff1, E N Pattishall, T G Gibbs, W DuMouchel, S J W Evans, N Yuen.   

Abstract

Robust tools for monitoring the safety of marketed therapeutic products are of paramount importance to public health. In recent years, innovative statistical approaches have been developed to screen large post-marketing safety databases for adverse events (AEs) that occur with disproportionate frequency. These methods, known variously as quantitative signal detection, disproportionality analysis, or safety data mining, facilitate the identification of new safety issues or possible harmful effects of a product. In this article, we describe the statistical concepts behind these methods, as well as their practical application to monitoring the safety of pharmaceutical products using spontaneous AE reports. We also provide examples of how these tools can be used to identify novel drug interactions and demographic risk factors for adverse drug reactions. Challenges, controversies, and frontiers for future research are discussed.

Entities:  

Mesh:

Year:  2007        PMID: 17538548     DOI: 10.1038/sj.clpt.6100258

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  83 in total

1.  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

2.  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

3.  Temporal data mining for adverse events following immunization in nationwide Danish healthcare databases.

Authors:  Henrik Svanström; Torbjörn Callréus; Anders Hviid
Journal:  Drug Saf       Date:  2010-11-01       Impact factor: 5.606

4.  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

5.  What Is the Plural of a 'Yellow' Anecdote?

Authors:  Stephen J W Evans
Journal:  Drug Saf       Date:  2016-01       Impact factor: 5.606

6.  Effects of stratification on data mining in the US Vaccine Adverse Event Reporting System (VAERS).

Authors:  Emily Jane Woo; Robert Ball; Dale R Burwen; M Miles Braun
Journal:  Drug Saf       Date:  2008       Impact factor: 5.606

7.  Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.

Authors:  Vishal N Patel; David C Kaelber
Journal:  J Biomed Inform       Date:  2013-10-28       Impact factor: 6.317

8.  Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles.

Authors:  Steven Bailey; Ajay Singh; Robert Azadian; Peter Huber; Michael Blum
Journal:  Drug Saf       Date:  2010-02-01       Impact factor: 5.606

9.  Stratification for spontaneous report databases.

Authors:  Stephen J W Evans
Journal:  Drug Saf       Date:  2008       Impact factor: 5.606

10.  Using information mining of the medical literature to improve drug safety.

Authors:  Kanaka D Shetty; Siddhartha R Dalal
Journal:  J Am Med Inform Assoc       Date:  2011-05-05       Impact factor: 4.497

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.