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

1 Citation (Scopus)

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
JournalPharmacoepidemiology and Drug Safety
DOIs
Publication statusPublished - 15 Jan 2019

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Pharmacoepidemiology
Pharmacovigilance
Conflict of Interest
Information Dissemination
Privacy
Research
Pharmaceutical Preparations
Population
Transparency

Cite this

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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.",
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How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias. / Platt, Robert W.; Platt, Richard; Brown, Jeffrey S.; Henry, David A.; Klungel, Olaf H.; Suissa, Samy.

In: Pharmacoepidemiology and Drug Safety, 15.01.2019.

Research output: Contribution to journalArticleResearchpeer-review

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