KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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CDISC Transformer: a metadata-based transformation tool for clinical trial and research data into CDISC standards

  • Park, Yu-Rang (Seoul National University Biomedical Informatics (SNUBI), Interdisciplinary Program of Medical Informatics and Systems Biomedical Informatics Research Center, Div. of Biomedical Informatics, Seoul National University College of Medicine) ;
  • Kim, Hye-Hyeon (Seoul National University Biomedical Informatics (SNUBI), Interdisciplinary Program of Medical Informatics and Systems Biomedical Informatics Research Center, Div. of Biomedical Informatics, Seoul National University College of Medicine) ;
  • Seo, Hwa-Jeong (Medical Informatics, Graduate School of Public Health, Gachon University of Medicine and Science) ;
  • Kim, Ju-Han (Seoul National University Biomedical Informatics (SNUBI), Interdisciplinary Program of Medical Informatics and Systems Biomedical Informatics Research Center, Div. of Biomedical Informatics, Seoul National University College of Medicine)
  • Received : 2011.03.31
  • Accepted : 2011.07.19
  • Published : 2011.10.31
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Abstract

CDISC (Clinical Data Interchanging Standards Consortium) standards are to support the acquisition, exchange, submission and archival of clinical trial and research data. SDTM (Study Data Tabulation Model) for Case Report Forms (CRFs) was recommended for U.S. Food and Drug Administration (FDA) regulatory submissions since 2004. Although the SDTM Implementation Guide gives a standardized and predefined collection of submission metadata 'domains' containing extensive variable collections, transforming CRFs to SDTM files for FDA submission is still a very hard and time-consuming task. For addressing this issue, we developed metadata based SDTM mapping rules. Using these mapping rules, we also developed a semi-automatic tool, named CDISC Transformer, for transforming clinical trial data to CDISC standard compliant data. The performance of CDISC Transformer with or without MDR support was evaluated using CDISC blank CRF as the 'gold standard'. Both MDR and user inquiry-supported transformation substantially improved the accuracy of our transformation rules. CDISC Transformer will greatly reduce the workloads and enhance standardized data entry and integration for clinical trial and research in various healthcare domains.

Keywords

References

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