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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Mar 11, 2023
Open Peer Review Period: Mar 11, 2023 - May 6, 2023
Date Accepted: Sep 14, 2023
Date Submitted to PubMed: Sep 28, 2023
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Matsuzaki K, Kitayama M, Yamamoto K, Aida R, Imai T, Ishida M, Katafichi R, Kawamura T, Yokoo T, Narita I, Suzuki Y

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

JMIR Med Inform 2023;11:e46725

DOI: 10.2196/46725

PMID: 38153801

PMCID: 10766166

A Pragmatic Method to Integrate Data from Pre-existing Cohort Studies using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Practical use of REDCap2SDTM

  • Keiichi Matsuzaki; 
  • Megumi Kitayama; 
  • Keiichi Yamamoto; 
  • Rei Aida; 
  • Takumi Imai; 
  • Mami Ishida; 
  • Ritsuko Katafichi; 
  • Tetsuya Kawamura; 
  • Takashi Yokoo; 
  • Ichiei Narita; 
  • Yusuke Suzuki

ABSTRACT

Background:

In recent years, many researchers have focused on legacy data utilization, such as pooled analyses that collect and re-analyze data from multiple studies. However, the methodology for the integration of pre-existing 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.

Objective:

To design a practical model for integrating pre-existing databases using the CDISC SDTM.

Methods:

Data integration was performed in three phases: i) confirmation of the variables, ii) SDTM mapping, and iii) generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the datasets were converted to vertical datasets. 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 REDCap field annotation. In phase 3, the data dictionary, including the SDTM metadata, were output in the Operational Data Model (ODM) format. Finally, the mapped SDTM were generated using REDCap2SDTM v2.

Results:

SDTM data were generated as a comma-separated values file for each of the seven domains defined in the metadata. Twenty-two items were commonly mapped to three databases. Because the SDTM data were set in each database correctly, we were able to integrate three independently pre-existing databases into one database in the CDISC SDTM format.

Conclusions:

Our project suggests that the CDISC SDTM is useful for integrating multiple pre-existing databases.


 Citation

Please cite as:

Matsuzaki K, Kitayama M, Yamamoto K, Aida R, Imai T, Ishida M, Katafichi R, Kawamura T, Yokoo T, Narita I, Suzuki Y

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

JMIR Med Inform 2023;11:e46725

DOI: 10.2196/46725

PMID: 38153801

PMCID: 10766166

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