Real-World Evidence BRIDGE: A Tool to Connect Protocol With Code Programming.

IF 2.4 4区医学 Q3 PHARMACOLOGY & PHARMACY

Pharmacoepidemiology and Drug Safety Pub Date : 2024-12-01 DOI:10.1002/pds.70062

Albert Cid Royo, Roel Elbers Jhj, Daniel Weibel, Vjola Hoxhaj, Zeynep Kurkcuoglu, Miriam C J Sturkenboom, Tiago A Vaz, Constanza L Andaur Navarro

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引用次数: 0

Abstract

Objective: To enhance documentation on programming decisions in Real World Evidence (RWE) studies.

Materials and methods: We analyzed several statistical analysis plans (SAP) within the Vaccine Monitoring Collaboration for Europe (VAC4EU) to identify study design sections and specifications for programming RWE studies. We designed a machine-readable metadata schema containing study sections, codelists, and time anchoring definitions specified in the SAPs with adaptability and user-friendliness.

Results: We developed the RWE-BRIDGE, a metadata schema in form of relational database divided into four study design sections with 12 tables: Study Variable Definition (two tables), Cohort Definition (two tables), Post-Exposure Outcome Analysis (one table), and Data Retrieval (seven tables). We provide a guide to populate this metadata schema and a Shiny app that checks the tables. RWE-BRIDGE is available on GitHub (github.com/UMC-Utrecht-RWE/RWE-BRIDGE).

Discussion: The RWE-BRIDGE has been designed to support the translation of study design sections from statistical analysis plans into analytical pipelines and to adhere to the FAIR principles, facilitating collaboration and transparency between researcher and programmers. This metadata schema strategy is flexible as it can support different common data models and programming languages, and it is adaptable to the specific needs of each SAP by adding further tables or fields, if necessary. Modified versions of the RWE-BRIGE have been applied in several RWE studies within VAC4EU.

Conclusion: RWE-BRIDGE offers a systematic approach to detailing variables, time anchoring, and algorithms for RWE studies. This metadata schema facilitates communication between researcher and programmers.

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真实世界的证据 BRIDGE：连接协议与代码编程的工具。

目的加强真实世界证据（RWE）研究中编程决策的记录：我们分析了欧洲疫苗监测合作组织（VAC4EU）的多个统计分析计划（SAP），以确定研究设计部分和 RWE 研究编程规范。我们设计了一种机器可读的元数据模式，其中包含 SAP 中指定的研究部分、编码表和时间锚定定义，具有适应性和用户友好性：我们开发了 RWE-BRIDGE，这是一个关系数据库形式的元数据模式，分为四个研究设计部分，共有 12 个表格：研究变量定义（两张表）、队列定义（两张表）、暴露后结果分析（一张表）和数据检索（七张表）。我们提供了一份填充元数据模式的指南和一个检查表的 Shiny 应用程序。RWE-BRIDGE 可在 GitHub 上下载（github.com/UMC-Utrecht-RWE/RWE-BRIDGE）：RWE-BRIDGE 的设计旨在支持将研究设计部分从统计分析计划转化为分析管道，并遵循 FAIR 原则，促进研究人员和程序员之间的协作和透明度。这种元数据模式策略非常灵活，因为它可以支持不同的通用数据模型和编程语言，而且可以根据每个 SAP 的具体需求进行调整，必要时还可以添加更多的表格或字段。RWE-BRIGE 的修改版已应用于 VAC4EU 的几项 RWE 研究中：RWE-BRIDGE 为 RWE 研究提供了详细说明变量、时间锚定和算法的系统方法。这种元数据模式有助于研究人员和程序员之间的交流。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Pharmacoepidemiology and Drug Safety 医学-药学

CiteScore

4.80

自引率

7.70%

发文量

173

审稿时长

3 months

期刊介绍： The aim of Pharmacoepidemiology and Drug Safety is to provide an international forum for the communication and evaluation of data, methods and opinion in the discipline of pharmacoepidemiology. The Journal publishes peer-reviewed reports of original research, invited reviews and a variety of guest editorials and commentaries embracing scientific, medical, statistical, legal and economic aspects of pharmacoepidemiology and post-marketing surveillance of drug safety. Appropriate material in these categories may also be considered for publication as a Brief Report. Particular areas of interest include: design, analysis, results, and interpretation of studies looking at the benefit or safety of specific pharmaceuticals, biologics, or medical devices, including studies in pharmacovigilance, postmarketing surveillance, pharmacoeconomics, patient safety, molecular pharmacoepidemiology, or any other study within the broad field of pharmacoepidemiology; comparative effectiveness research relating to pharmaceuticals, biologics, and medical devices. Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, as these methods are truly used in the real world; methodologic contributions of relevance to pharmacoepidemiology, whether original contributions, reviews of existing methods, or tutorials for how to apply the methods of pharmacoepidemiology; assessments of harm versus benefit in drug therapy; patterns of drug utilization; relationships between pharmacoepidemiology and the formulation and interpretation of regulatory guidelines; evaluations of risk management plans and programmes relating to pharmaceuticals, biologics and medical devices.