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Pharmacoepidemiol Drug Saf. 2019 Jan 15. doi: 10.1002/pds.4722. [Epub ahead of print]

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

Author information

1
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
2
Centre for Clinical Epidemiology, Lady Davis Research Institute of the Jewish General Hospital, Montreal, Canada.
3
Centre for Health Outcomes Research, Research Institute of the McGill University Health Centre, Montreal, Canada.
4
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
5
Centre for Research in Evidence-based practice, Bond University, Gold Coast, Australia.
6
Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
7
Institute for Clinical and Evaluative Sciences, Toronto, Canada.
8
Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
9
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.

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.

KEYWORDS:

bias; common data model; distributed networks; pharmacoepidemiology; protocol

PMID:
30648307
DOI:
10.1002/pds.4722

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