Aetion, New York, New York, USA.
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
Pharmacoepidemiol Drug Saf. 2022 Nov;31(11):1140-1152. doi: 10.1002/pds.5529. Epub 2022 Sep 9.
Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study-from initial study design to final report-to facilitate rationalized and transparent decision-making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.
透明度越来越多地被提倡用于在使用真实世界数据的非随机研究中建立信任。图形和数据可视化通过帮助沟通和理解来支持透明度,并可以为研究设计和分析决策提供信息。然而,除了研究设计和流程图(例如,类似于 CONSORT 的图表)的图形表示之外,还需要关于如何通过可视化实现最大有效性和透明度的具体标准。本文提供了在药物流行病学研究的整个生命周期中使用可视化的指南-从最初的研究设计到最终报告-以促进对研究设计和实施的合理化和透明决策,并清晰地传达研究结果。我们的目的是帮助研究人员使其做法与当前关于透明度的共识声明保持一致。