A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study

• Published in: JMIR medical informatics 2023-12, Vol.11, p.e46725-e46725
• Main Authors: Matsuzaki, Keiichi, Kitayama, Megumi, Yamamoto, Keiichi, Aida, Rei, Imai, Takumi, Ishida, Mami, Katafuchi, Ritsuko, Kawamura, Tetsuya, Yokoo, Takashi, Narita, Ichiei, Suzuki, Yusuke
• Format: Article
• Language: English
• Subjects: Case reports; Clinical Informatics; Clinical trials; Codes; Cohort analysis; Consortia; Creatinine; Data Science; Datasets; Digital Health Reporting Standards, Quality and Transparency in e-Research; Disease; Females; Information sharing; Innovations in Clinical Trials and Research Data Management; Metadata; New Methods; Original Paper; Secondary Use of Clinical Data for Research and Surveillance; Variables
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/46725

In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.