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

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Summary
This summary is machine-generated.

We developed RWE-BRIDGE, a metadata schema to improve programming documentation in Real World Evidence (RWE) studies. This system enhances transparency and collaboration between researchers and programmers.

Keywords:
drug safetydrug utilizationelectronic health recordspharmacoepidemiologyvaccines

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Area of Science:

  • Health Informatics
  • Data Management
  • Pharmacovigilance

Background:

  • Documentation of programming decisions in Real World Evidence (RWE) studies is crucial for transparency and reproducibility.
  • Existing methods for documenting RWE study designs can be fragmented and lack standardization.
  • The Vaccine Monitoring Collaboration for Europe (VAC4EU) initiative highlighted the need for improved RWE study documentation.

Purpose of the Study:

  • To enhance the documentation of programming decisions in Real World Evidence (RWE) studies.
  • To develop a machine-readable metadata schema for RWE study designs.
  • To facilitate the translation of study designs into analytical pipelines.

Main Methods:

  • Analyzed statistical analysis plans (SAPs) from the VAC4EU initiative.
  • Designed a machine-readable metadata schema (RWE-BRIDGE) with study sections, codelists, and time anchoring.
  • Developed a guide and a Shiny app for populating and validating the metadata schema.

Main Results:

  • Developed RWE-BRIDGE, a relational database schema with 12 tables across four study design sections.
  • The schema includes definitions for study variables, cohorts, outcomes, and data retrieval.
  • RWE-BRIDGE is available on GitHub and has been applied in several RWE studies within VAC4EU.

Conclusions:

  • RWE-BRIDGE provides a systematic approach to detailing RWE study parameters, including variables, time anchoring, and algorithms.
  • The metadata schema promotes FAIR principles, enhancing collaboration and transparency between researchers and programmers.
  • RWE-BRIDGE is flexible, adaptable to various data models and programming languages, and supports specific SAP needs.