Real-World Evidence BRIDGE: a tool to connect protocol with code programming

Abstract

AbstractObjectiveMethodsSeveral statistical analysis plans (SAP) from the Vaccine Monitoring Collaboration for Europe (VAC4EU) were analyzed to identify the study design sections and specifications for programming RWE studies based on multi-databases standardized to common data models. We envisioned a metadata schema that transforms the epidemiologist’s knowledge into a machine-readable format. This machine-readable metadata schema must also contain the different study sections, code lists, and time anchoring specified in the SAPs. Further desired attributes are adaptability and user-friendliness.ResultsWe developed RWE-BRIDGE, a metadata schema with a star-schema model divided into four study design sections with 12 tables: Study Variable Definition with two tables, Cohort Definition with two tables, Post-Exposure Outcome Analysis with one table, and Data Retrieval with seven tables. We provide examples and a step-by-step guide to populate this metadata schema. In addition, we provide a Shiny app that checks the several tables proposed in this metadata strategy. RWE-BRIDGE is available athttps://github.com/UMC-Utrecht-RWE/RWE-BRIDGE.DiscussionThe RWE-BRIDGE has been designed to support the translation of study design sections from statistical analysis plans into analytical pipelines, facilitating collaboration and transparency between lead researchers and scientific programmers and reducing hard coding and repetition. This metadata schema strategy is flexible by supporting 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 the VAC4EU ecosystem.ConclusionThe RWE-BRIDGE offers a systematic approach to detailing what type of variables, time anchoring, and algorithms are required for a specific RWE study. Applying this metadata schema can facilitate the communication between epidemiologists and programmers in a transparent manner.