Literature DB >> 22262618

A protocol for active surveillance of acute myocardial infarction in association with the use of a new antidiabetic pharmaceutical agent.

Bruce Fireman1, Sengwee Toh, Melissa G Butler, Alan S Go, Hylton V Joffe, David J Graham, Jennifer C Nelson, Gregory W Daniel, Joe V Selby.   

Abstract

PURPOSE: To describe a protocol for active surveillance of acute myocardial infarction (AMI) in users of a recently approved oral antidiabetic medication, saxagliptin, and to provide the rationale for decisions made in drafting the protocol.
METHODS: A new-user cohort design is planned for evaluating data from at least four Mini-Sentinel data partners from 1 August 2009 (following US Food and Drug Administration's approval of saxagliptin) through mid-2013. New users of saxagliptin will be compared in separate analyses with new users of sitagliptin, pioglitazone, long-acting insulins, and second-generation sulfonylureas. Two approaches to controlling for confounding will be evaluated: matching by exposure propensity score and stratification by AMI risk score. The primary analyses will use Cox regression models specified in a way that does not require pooling of patient-level data from the data partners. The Cox models are fit to summarized data on risk sets composed of saxagliptin users and similar comparator users at the time of an AMI. Secondary analyses will use alternative methods including Poisson regression and will explore whether further adjustment for covariates available only at some data partners (e.g., blood pressure) modifies results.
RESULTS: The results of this study are pending.
CONCLUSIONS: The proposed protocol describes a design for surveillance to evaluate the safety of a newly marketed agent as postmarket experience accrues. It uses data from multiple partner organizations without requiring sharing of patient-level data and compares alternative approaches to controlling for confounding. It is hoped that this initial active surveillance project of the Mini-Sentinel will provide insights that inform future population-based surveillance of medical product safety.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22262618     DOI: 10.1002/pds.2337

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  10 in total

1.  Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system.

Authors:  Patrick B Ryan; Martijn J Schuemie; Susan Gruber; Ivan Zorych; David Madigan
Journal:  Drug Saf       Date:  2013-10       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.  Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal.

Authors:  Martin Kulldorff; Ivair R Silva
Journal:  Revstat Stat J       Date:  2017-07       Impact factor: 0.985

4.  Risk for Hospitalized Heart Failure Among New Users of Saxagliptin, Sitagliptin, and Other Antihyperglycemic Drugs: A Retrospective Cohort Study.

Authors:  Sengwee Toh; Christian Hampp; Marsha E Reichman; David J Graham; Suchitra Balakrishnan; Frank Pucino; Jack Hamilton; Samuel Lendle; Aarthi Iyer; Malcolm Rucker; Madelyn Pimentel; Neesha Nathwani; Marie R Griffin; Nancy J Brown; Bruce H Fireman
Journal:  Ann Intern Med       Date:  2016-04-26       Impact factor: 25.391

5.  Near Real-time Surveillance for Consequences of Health Policies Using Sequential Analysis.

Authors:  Christine Y Lu; Robert B Penfold; Sengwee Toh; Jessica L Sturtevant; Jeanne M Madden; Gregory Simon; Brian K Ahmedani; Gregory Clarke; Karen J Coleman; Laurel A Copeland; Yihe G Daida; Robert L Davis; Enid M Hunkeler; Ashli Owen-Smith; Marsha A Raebel; Rebecca Rossom; Stephen B Soumerai; Martin Kulldorff
Journal:  Med Care       Date:  2018-05       Impact factor: 2.983

6.  Assessing the impact of propensity score estimation and implementation on covariate balance and confounding control within and across important subgroups in comparative effectiveness research.

Authors:  Cynthia J Girman; Mugdha Gokhale; Tzuyung Doug Kou; Kimberly G Brodovicz; Richard Wyss; Til Stürmer
Journal:  Med Care       Date:  2014-03       Impact factor: 2.983

7.  Validity of Privacy-Protecting Analytical Methods That Use Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks.

Authors:  Xiaojuan Li; Bruce H Fireman; Jeffrey R Curtis; David E Arterburn; David P Fisher; Érick Moyneur; Mia Gallagher; Marsha A Raebel; W Benjamin Nowell; Lindsay Lagreid; Sengwee Toh
Journal:  Am J Epidemiol       Date:  2019-04-01       Impact factor: 4.897

8.  Network meta-analysis incorporating randomized controlled trials and non-randomized comparative cohort studies for assessing the safety and effectiveness of medical treatments: challenges and opportunities.

Authors:  Chris Cameron; Bruce Fireman; Brian Hutton; Tammy Clifford; Doug Coyle; George Wells; Colin R Dormuth; Robert Platt; Sengwee Toh
Journal:  Syst Rev       Date:  2015-11-05

9.  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

10.  Risk of heart failure hospitalization among users of dipeptidyl peptidase-4 inhibitors compared to glucagon-like peptide-1 receptor agonists.

Authors:  Ghadeer K Dawwas; Steven M Smith; Haesuk Park
Journal:  Cardiovasc Diabetol       Date:  2018-07-17       Impact factor: 9.951

  10 in total

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