Literature DB >> 21041580

Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

Paul E Stang1, Patrick B Ryan, Judith A Racoosin, J Marc Overhage, Abraham G Hartzema, Christian Reich, Emily Welebob, Thomas Scarnecchia, Janet Woodcock.   

Abstract

The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.

Mesh:

Year:  2010        PMID: 21041580     DOI: 10.7326/0003-4819-153-9-201011020-00010

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  159 in total

1.  Validation of a common data model for active safety surveillance research.

Authors:  J Marc Overhage; Patrick B Ryan; Christian G Reich; Abraham G Hartzema; Paul E Stang
Journal:  J Am Med Inform Assoc       Date:  2011-10-28       Impact factor: 4.497

2.  Translational informatics: an industry perspective.

Authors:  Michael N Cantor
Journal:  J Am Med Inform Assoc       Date:  2012-01-11       Impact factor: 4.497

3.  Design and validation of a data simulation model for longitudinal healthcare data.

Authors:  Richard E Murray; Patrick B Ryan; Stephanie J Reisinger
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

4.  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

5.  Early steps in the development of a claims-based targeted healthcare safety monitoring system and application to three empirical examples.

Authors:  Peter M Wahl; Joshua J Gagne; Thomas E Wasser; Debra F Eisenberg; J Keith Rodgers; Gregory W Daniel; Marcus Wilson; Sebastian Schneeweiss; Jeremy A Rassen; Amanda R Patrick; Jerry Avorn; Rhonda L Bohn
Journal:  Drug Saf       Date:  2012-05-01       Impact factor: 5.606

6.  Social Media Listening for Routine Post-Marketing Safety Surveillance.

Authors:  Gregory E Powell; Harry A Seifert; Tjark Reblin; Phil J Burstein; James Blowers; J Alan Menius; Jeffery L Painter; Michele Thomas; Carrie E Pierce; Harold W Rodriguez; John S Brownstein; Clark C Freifeld; Heidi G Bell; Nabarun Dasgupta
Journal:  Drug Saf       Date:  2016-05       Impact factor: 5.606

7.  Text mining for adverse drug events: the promise, challenges, and state of the art.

Authors:  Rave Harpaz; Alison Callahan; Suzanne Tamang; Yen Low; David Odgers; Sam Finlayson; Kenneth Jung; Paea LePendu; Nigam H Shah
Journal:  Drug Saf       Date:  2014-10       Impact factor: 5.606

8.  Model Misspecification When Excluding Instrumental Variables From PS Models in Settings Where Instruments Modify the Effects of Covariates on Treatment.

Authors:  Richard Wyss; Alan R Ellis; Mark Lunt; M Alan Brookhart; Robert J Glynn; Til Stürmer
Journal:  Epidemiol Methods       Date:  2014-12

9.  Using i2b2 to Bootstrap Rural Health Analytics and Learning Networks.

Authors:  Daniel R Harris; Adam D Baus; Tamela J Harper; Traci D Jarrett; Cecil R Pollard; Jeffery C Talbert
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2016-08

10.  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

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