Literature DB >> 29714520

Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance.

Rima Izem1, Matilde Sanchez-Kam2, Haijun Ma3, Richard Zink4, Yueqin Zhao1.   

Abstract

BACKGROUND: Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product's safety profile continually evolves as safety data accumulate.
METHODS: This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group.
RESULTS: We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources.
CONCLUSIONS: Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Entities:  

Keywords:  adverse events; data mining; passive surveillance; postmarket surveillance; signal detection

Mesh:

Year:  2018        PMID: 29714520      PMCID: PMC5987777          DOI: 10.1177/2168479017741112

Source DB:  PubMed          Journal:  Ther Innov Regul Sci        ISSN: 2168-4790            Impact factor:   1.778


  36 in total

1.  A tree-based scan statistic for database disease surveillance.

Authors:  Martin Kulldorff; Zixing Fang; Stephen J Walsh
Journal:  Biometrics       Date:  2003-06       Impact factor: 2.571

2.  Comparing data mining methods on the VAERS database.

Authors:  David Banks; Emily Jane Woo; Dale R Burwen; Phil Perucci; M Miles Braun; Robert Ball
Journal:  Pharmacoepidemiol Drug Saf       Date:  2005-09       Impact factor: 2.890

Review 3.  Data mining in spontaneous reports.

Authors:  Andrew Bate; I R Edwards
Journal:  Basic Clin Pharmacol Toxicol       Date:  2006-03       Impact factor: 4.080

4.  A Bayesian neural network method for adverse drug reaction signal generation.

Authors:  A Bate; M Lindquist; I R Edwards; S Olsson; R Orre; A Lansner; R M De Freitas
Journal:  Eur J Clin Pharmacol       Date:  1998-06       Impact factor: 2.953

5.  When to publish measures of disproportionality derived from spontaneous reporting databases?

Authors:  Anthonius de Boer
Journal:  Br J Clin Pharmacol       Date:  2011-12       Impact factor: 4.335

Review 6.  Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project.

Authors:  Vaishali K Patadia; Preciosa Coloma; Martijn J Schuemie; Ron Herings; Rosa Gini; Giampiero Mazzaglia; Gino Picelli; Carla Fornari; Lars Pedersen; Johan van der Lei; Miriam Sturkenboom; Gianluca Trifirò
Journal:  Expert Rev Clin Pharmacol       Date:  2015-01       Impact factor: 5.045

7.  Vaccine adverse event text mining system for extracting features from vaccine safety reports.

Authors:  Taxiarchis Botsis; Thomas Buttolph; Michael D Nguyen; Scott Winiecki; Emily Jane Woo; Robert Ball
Journal:  J Am Med Inform Assoc       Date:  2012-08-25       Impact factor: 4.497

8.  Likelihood ratio based tests for longitudinal drug safety data.

Authors:  Lan Huang; Jyoti Zalkikar; Ram Tiwari
Journal:  Stat Med       Date:  2014-02-09       Impact factor: 2.373

Review 9.  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

Review 10.  Novel statistical tools for monitoring the safety of marketed drugs.

Authors:  J S Almenoff; E N Pattishall; T G Gibbs; W DuMouchel; S J W Evans; N Yuen
Journal:  Clin Pharmacol Ther       Date:  2007-05-30       Impact factor: 6.875

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  1 in total

Review 1.  Pharmacovigilance: reporting requirements throughout a product's lifecycle.

Authors:  Sylvia Lucas; Jessica Ailani; Timothy R Smith; Ahmad Abdrabboh; Fei Xue; Marco S Navetta
Journal:  Ther Adv Drug Saf       Date:  2022-09-27
  1 in total

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