Platt Robert W, Platt Richard, Brown Jeffrey S, Henry David A, Klungel Olaf H, Suissa Samy
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
Centre for Clinical Epidemiology, Lady Davis Research Institute of the Jewish General Hospital, Montreal, Canada.
Pharmacoepidemiol Drug Saf. 2019 Jan 15. doi: 10.1002/pds.4722.
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.
在过去十年间，已经建立了几个药物流行病学网络，这些网络采用分布式方法，在多个数据站点进行相同的分析，以保护隐私并尽量减少数据共享。分布式网络通过对非常大的人群数据进行调查，效率很高。这些网络的结构还可用于提高可重复性、增加透明度并减少偏差。我们以加拿大药物效应观察研究网络、美国哨兵系统以及欧洲药物警戒和药物流行病学研究网络为例，描述分布式网络的一些特征。通用协议、分析计划和数据模型，以及关于修订和违反协议的政策，是其关键特征。这些工具确保研究可以根据需要进行审核和重复。盲法和严格的利益冲突政策减少了分析和解释中出现偏差的可能性。这些进展应能提高用于支持临床和监管决策的信息的及时性和准确性。
