Pharmacoepidemiology and drug safety · 2026
CodeMergeR: A Shiny-Based Application for Codelist Integration.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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История · было → стало
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Hoxhaj Vjola, Riera-Arnau Judit, Mohammadi Sima, Sturkenboom Miriam C J M, Andaur Navarro Constanza L
Аннотация
OBJECTIVE: To describe CodeMergeR, an open-source R shiny application developed within the VAC4EU network for the standardization, cleaning, and integration of individual concept codelists into a master file for real-world evidence (RWE) studies.
MATERIALS AND METHODS: CodeMergeR was designed to process codelists exported by CodeMapper v1.0. The application includes two modules: (1) Conformance and Coherence, which checks file naming conventions, metadata consistency, and structural integrity; and (2) Cleaning and Standardization, which removes duplicates, corrects formatting issues where a valid match is available, flags and excludes uncorrectable codes (e.g., scientific notation, dashes). Functional and performance testing were conducted using real-world library metadata and codelists from the VAC4EU library, including manually introduced errors to assess detection capabilities.
RESULTS: The CodeMergeR identified a wide range of structural and semantic issues, achieving an overall error detection rate of 96.8% (61/63 errors). Depending on the issue type, detected errors were either automatically corrected (e.g., rounding, when a validated code was found), flagged and excluded from the master codelist if unresolved (e.g., scientific notation, dashes), or flagged for manual review (e.g., metadata mismatches, missing concepts). Processing of 50 clinical concept folders and 10 Algorithms completed in under 1 min during performance testing.
DISCUSSION: CodeMergeR detected structural and formatting errors with an accuracy of 96.8%, supporting its use as a quality-check step prior to deploying codelists in analytical pipelines. In contrast to existing, CodeMergeR fills a distinct quality assurance role in codelist generation and transparency.
CONCLUSION: CodeMergeR contributes to improved reproducibility, scalability, and transparency in codelist management in RWE studies.
MATERIALS AND METHODS: CodeMergeR was designed to process codelists exported by CodeMapper v1.0. The application includes two modules: (1) Conformance and Coherence, which checks file naming conventions, metadata consistency, and structural integrity; and (2) Cleaning and Standardization, which removes duplicates, corrects formatting issues where a valid match is available, flags and excludes uncorrectable codes (e.g., scientific notation, dashes). Functional and performance testing were conducted using real-world library metadata and codelists from the VAC4EU library, including manually introduced errors to assess detection capabilities.
RESULTS: The CodeMergeR identified a wide range of structural and semantic issues, achieving an overall error detection rate of 96.8% (61/63 errors). Depending on the issue type, detected errors were either automatically corrected (e.g., rounding, when a validated code was found), flagged and excluded from the master codelist if unresolved (e.g., scientific notation, dashes), or flagged for manual review (e.g., metadata mismatches, missing concepts). Processing of 50 clinical concept folders and 10 Algorithms completed in under 1 min during performance testing.
DISCUSSION: CodeMergeR detected structural and formatting errors with an accuracy of 96.8%, supporting its use as a quality-check step prior to deploying codelists in analytical pipelines. In contrast to existing, CodeMergeR fills a distinct quality assurance role in codelist generation and transparency.
CONCLUSION: CodeMergeR contributes to improved reproducibility, scalability, and transparency in codelist management in RWE studies.