How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias

Robert W. Platt, Richard Platt, Jeffrey S. Brown, David A. Henry, Olaf H. Klungel, Samy Suissa

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)
43 Downloads (Pure)

Abstract

Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing. Distributed networks are efficient, by interrogating data on very large populations. The structure of these networks can also be leveraged to improve replicability, increase transparency, and reduce bias. We describe some features of distributed networks using, as examples, the Canadian Network for Observational Drug Effect Studies, the Sentinel System in the USA, and the European Research Network of Pharmacovigilance and Pharmacoepidemiology. Common protocols, analysis plans, and data models, with policies on amendments and protocol violations, are key features. These tools ensure that studies can be audited and repeated as necessary. Blinding and strict conflict of interest policies reduce the potential for bias in analyses and interpretation. These developments should improve the timeliness and accuracy of information used to support both clinical and regulatory decisions.

Original languageEnglish
Pages (from-to)3-7
Number of pages5
JournalPharmacoepidemiology and Drug Safety
Volume29
Issue numberS1
Early online date15 Jan 2019
DOIs
Publication statusPublished - 1 Jan 2020

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