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.
| Language | English |
|---|---|
| Journal | Pharmacoepidemiology and Drug Safety |
| DOIs | |
| Publication status | Published - 15 Jan 2019 |
Fingerprint
Cite this
}
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 journal › Article › Research › peer-review
TY - JOUR
T1 - How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias
AU - Platt, Robert W.
AU - Platt, Richard
AU - Brown, Jeffrey S.
AU - Henry, David A.
AU - Klungel, Olaf H.
AU - Suissa, Samy
PY - 2019/1/15
Y1 - 2019/1/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060159088&partnerID=8YFLogxK
U2 - 10.1002/pds.4722
DO - 10.1002/pds.4722
M3 - Article
JO - Pharmacoepidemiology and Drug Safety
T2 - Pharmacoepidemiology and Drug Safety
JF - Pharmacoepidemiology and Drug Safety
SN - 1053-8569
ER -