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How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias
Author(s) -
Platt Robert W.,
Platt Richard,
Brown Jeffrey S.,
Henry David A.,
Klungel Olaf H.,
Suissa Samy
Publication year - 2020
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4722
Subject(s) - pharmacoepidemiology , transparency (behavior) , audit , pharmacovigilance , observational study , medicine , protocol (science) , comparative effectiveness research , data science , computer science , data mining , risk analysis (engineering) , accounting , computer security , pharmacology , drug , alternative medicine , business , pathology , medical prescription
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.