Data ManagementGlossary Term
An international nonprofit organization that develops and supports global data standards for clinical research, enabling consistent and efficient exchange of clinical trial information.
The Clinical Data Interchange Standards Consortium has established itself as the primary source of data standards for the clinical research industry, developing comprehensive frameworks that specify how clinical data should be structured, formatted, and exchanged. These standards facilitate interoperability between different systems and organizations, improve data quality through standardized definitions and formats, and enable regulatory agencies to more efficiently review and analyze submitted data. Adoption of CDISC standards has become mandatory or strongly encouraged by major regulatory agencies worldwide.
The CDISC family of standards addresses different stages and aspects of clinical research data. The Study Data Tabulation Model defines the standard format for submitting raw clinical data to regulatory authorities. The Analysis Data Model specifies how analysis-ready datasets should be structured. The Operational Data Model provides standards for electronic data capture and exchange during study conduct. The Protocol Representation Model enables structured exchange of protocol information. These interconnected standards create a coherent framework spanning the entire clinical data lifecycle.
Implementation of CDISC standards requires significant investment in technical infrastructure, process development, and personnel training. Organizations must develop controlled terminology mapping, create or configure systems capable of generating compliant outputs, establish validation processes to verify standard compliance, and train staff on applicable requirements. However, these investments yield benefits including improved data quality, reduced rework during regulatory submission preparation, enhanced ability to pool and compare data across studies, and compliance with regulatory expectations. CDISC provides implementation guides, training resources, and support tools to facilitate adoption.
Regulatory submission
"In preparation for the NDA submission, the data management team converted all clinical datasets to CDISC SDTM format and validated compliance with FDA technical specifications to ensure the submission would pass technical review."
Standards implementation
"The sponsor invested in upgrading its EDC system and training data managers on CDISC standards to ensure that new studies would collect data in formats readily convertible to submission-ready datasets."
A CDISC standard that defines the structure and content of analysis-ready datasets derived from SDTM data, supporting efficient generation of statistical analyses and displays for regulatory submissions.
A secure, computer-generated, time-stamped electronic record that automatically captures the creation, modification, or deletion of data, including the identity of the operator and the date and time of the action.
The process of detecting, correcting, and resolving inaccurate, incomplete, or inconsistent data in the clinical trial database to ensure data quality and reliability for analysis.
The degree to which data are complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle.
The formal process of making the clinical trial database unmodifiable once all data have been entered, reviewed, cleaned, and verified, marking the transition from data collection to statistical analysis.
An international nonprofit organization that develops and supports global data standards for clinical research, enabling consistent and efficient exchange of clinical trial information.
The Clinical Data Interchange Standards Consortium has established itself as the primary source of data standards for the clinical research industry, developing comprehensive frameworks that specify how clinical data should be structured, formatted, and exchanged. These standards facilitate interoperability between different systems and organizations, improve data quality through standardized definitions and formats, and enable regulatory agencies to more efficiently review and analyze submitted data. Adoption of CDISC standards has become mandatory or strongly encouraged by major regulatory agencies worldwide.
The CDISC family of standards addresses different stages and aspects of clinical research data. The Study Data Tabulation Model defines the standard format for submitting raw clinical data to regulatory authorities. The Analysis Data Model specifies how analysis-ready datasets should be structured. The Operational Data Model provides standards for electronic data capture and exchange during study conduct. The Protocol Representation Model enables structured exchange of protocol information. These interconnected standards create a coherent framework spanning the entire clinical data lifecycle.
Implementation of CDISC standards requires significant investment in technical infrastructure, process development, and personnel training. Organizations must develop controlled terminology mapping, create or configure systems capable of generating compliant outputs, establish validation processes to verify standard compliance, and train staff on applicable requirements. However, these investments yield benefits including improved data quality, reduced rework during regulatory submission preparation, enhanced ability to pool and compare data across studies, and compliance with regulatory expectations. CDISC provides implementation guides, training resources, and support tools to facilitate adoption.
Regulatory submission
"In preparation for the NDA submission, the data management team converted all clinical datasets to CDISC SDTM format and validated compliance with FDA technical specifications to ensure the submission would pass technical review."
Standards implementation
"The sponsor invested in upgrading its EDC system and training data managers on CDISC standards to ensure that new studies would collect data in formats readily convertible to submission-ready datasets."
A CDISC standard that defines the structure and content of analysis-ready datasets derived from SDTM data, supporting efficient generation of statistical analyses and displays for regulatory submissions.
A secure, computer-generated, time-stamped electronic record that automatically captures the creation, modification, or deletion of data, including the identity of the operator and the date and time of the action.
The process of detecting, correcting, and resolving inaccurate, incomplete, or inconsistent data in the clinical trial database to ensure data quality and reliability for analysis.
The degree to which data are complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle.
The formal process of making the clinical trial database unmodifiable once all data have been entered, reviewed, cleaned, and verified, marking the transition from data collection to statistical analysis.