Literature DB >> 22138688

Methods for observational post-licensure medical product safety surveillance.

Jennifer C Nelson1, Andrea J Cook2, Onchee Yu3, Shanshan Zhao2, Lisa A Jackson4, Bruce M Psaty5.   

Abstract

Post-licensure medical product safety surveillance is important for detecting adverse events potentially not identified pre-licensure. Historically, post-licensure safety monitoring has been accomplished using passive reporting systems and by conducting formal Phase IV randomized trials or large epidemiological studies, also known as safety surveillance or pharmacovigilance studies. However, crucial gaps in the safety evidence base provided by these approaches have led to high profile product withdrawals and growing public concern about unknown health risks associated with licensed products. To address the limitations of existing surveillance systems and to facilitate more accurate and rapid detection of safety problems, new systems involving active surveillance of large, population-based cohorts using observational health care databases are being developed. In this article, we review common statistical methods that have been employed previously for post-licensure safety monitoring, including data mining and sequential hypothesis testing, and assess which methods may be promising for potential use within this newly proposed prospective observational cohort monitoring framework. We discuss gaps in existing approaches and identify areas where methodological development is needed to improve the success of safety surveillance efforts in this setting.
© The Author(s) 2011 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Keywords:  data mining; observational study; post-licensure safety; sequential testing

Mesh:

Year:  2011        PMID: 22138688     DOI: 10.1177/0962280211413452

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  10 in total

1.  Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study.

Authors:  Christian Hoppe; Patrick Obermeier; Susann Muehlhans; Maren Alchikh; Lea Seeber; Franziska Tief; Katharina Karsch; Xi Chen; Sindy Boettcher; Sabine Diedrich; Tim Conrad; Bron Kisler; Barbara Rath
Journal:  Drug Saf       Date:  2016-10       Impact factor: 5.606

2.  Using Multiple Pharmacovigilance Models Improves the Timeliness of Signal Detection in Simulated Prospective Surveillance.

Authors:  Rolina D van Gaalen; Michal Abrahamowicz; David L Buckeridge
Journal:  Drug Saf       Date:  2017-11       Impact factor: 5.606

3.  Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets.

Authors:  Meng Wang; Zhanglong Ji; Hyeon-Eui Kim; Shuang Wang; Li Xiong; Xiaoqian Jiang
Journal:  IEEE Trans Knowl Data Eng       Date:  2017-11-14       Impact factor: 6.977

Review 4.  Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance.

Authors:  Rima Izem; Matilde Sanchez-Kam; Haijun Ma; Richard Zink; Yueqin Zhao
Journal:  Ther Innov Regul Sci       Date:  2018-01-08       Impact factor: 1.778

5.  Comment on Ellenberg and Morris: The role of statisticians in vaccine surveillance.

Authors:  Robert W Platt
Journal:  Stat Med       Date:  2021-05-20       Impact factor: 2.373

6.  Use of routinely collected electronic healthcare data for postlicensure vaccine safety signal detection: a systematic review.

Authors:  Yonatan Moges Mesfin; Allen Cheng; Jock Lawrie; Jim Buttery
Journal:  BMJ Glob Health       Date:  2019-07-08

7.  Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies.

Authors:  A Anil Sinaci; Gokce B Laleci Erturkmen; Suat Gonul; Mustafa Yuksel; Paolo Invernizzi; Bharat Thakrar; Anil Pacaci; H Alper Cinar; Nihan Kesim Cicekli
Journal:  Biomed Res Int       Date:  2015-10-12       Impact factor: 3.411

8.  A Synthesis of Current Surveillance Planning Methods for the Sequential Monitoring of Drug and Vaccine Adverse Effects Using Electronic Health Care Data.

Authors:  Jennifer C Nelson; Robert Wellman; Onchee Yu; Andrea J Cook; Judith C Maro; Rita Ouellet-Hellstrom; Denise Boudreau; James S Floyd; Susan R Heckbert; Simone Pinheiro; Marsha Reichman; Azadeh Shoaibi
Journal:  EGEMS (Wash DC)       Date:  2016-09-06

Review 9.  Near real-time vaccine safety surveillance using electronic health records-a systematic review of the application of statistical methods.

Authors:  Andreia Leite; Nick J Andrews; Sara L Thomas
Journal:  Pharmacoepidemiol Drug Saf       Date:  2016-01-28       Impact factor: 2.890

10.  Benefit-Risk Monitoring of Vaccines Using an Interactive Dashboard: A Methodological Proposal from the ADVANCE Project.

Authors:  Kaatje Bollaerts; Tom De Smedt; Katherine Donegan; Lina Titievsky; Vincent Bauchau
Journal:  Drug Saf       Date:  2018-08       Impact factor: 5.606

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.