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A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards

Keiichi Matsuzaki1, Megumi Kitayama2, Keiichi Yamamoto3

  • 1Department of Public Health, School of Medicine, Kitasato University, Sagamihara, Japan.

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|December 28, 2023
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Summary

Integrating legacy data is challenging. This study demonstrates that Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) effectively integrates multiple preexisting databases, simplifying pooled analyses.

Keywords:
CDISCClinical Data Interchange Standards ConsortiumSDTMStudy Data Tabulation Modeldata managementdata warehousingdatabase integrationintegrate multiple data sets

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Area of Science:

  • Data Science
  • Biomedical Informatics
  • Clinical Research Informatics

Background:

  • Legacy data integration presents methodological challenges.
  • Standardized data formats are crucial for reanalyzing diverse datasets.
  • Previous work developed tools for generating SDTM data from hypothetical trials.

Purpose of the Study:

  • To design a practical model for integrating preexisting databases.
  • To leverage the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) for data harmonization.
  • To establish a reproducible methodology for combining data collected for different purposes.

Main Methods:

  • Data integration involved variable confirmation, SDTM mapping, and SDTM data generation.
  • Metadata including domain name, variable name, and test code were embedded in Research Electronic Data Capture (REDCap) annotations.
  • The Operational Data Model (ODM) format was utilized for the data dictionary, with REDCap2SDTM version 2 employed for final data generation.

Main Results:

  • SDTM data were successfully generated for 7 domains across 3 independently preexisting databases.
  • A total of 17 common items were mapped, demonstrating successful harmonization.
  • Three disparate databases were integrated into a single, standardized CDISC SDTM format database.

Conclusions:

  • The CDISC SDTM provides a robust framework for integrating multiple preexisting databases.
  • This methodology facilitates the efficient pooling and reanalysis of legacy data.
  • The developed model offers a practical solution for harmonizing diverse clinical trial datasets.