CDISC Legacy Data Conversions - Overview
Study data is information about a person in a clinical trial. It includes demographic information, details of the medical treatment, descriptions of the participant's progress, and other relevant information. If the same attribute information is captured for animals, it is considered non-clinical data. Studies suggest that certain standard methods are ideal for the exchange of clinical and non-clinical research data among computer systems. The United States Food and Drug Administration (USFDA) may refuse to file New Drug Applications (NDA) or Biological License Applications (BLAs), and may refuse to receive Abbreviated New Drug Applications (ANDA) if the study data submitted to the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) is not on par with the data standards. As part of a collaborative effort between the FDA and the nonprofit Clinical Data Interchange Standards Consortium (CDISC), the following study data standards have been developed for effective legacy data conversions:
- CDISC Legacy Data Conversions Standard for Exchange of Nonclinical Data (SEND) for nonclinical data
- CDISC Legacy Data Conversions Study Data Tabulation Model (SDTM) for clinical data standards
- CDISC Legacy Data Conversions Analysis Data Model (ADaM) for analysis of clinical data standards
- CDISC Legacy Data Conversions Case Report Tabulation Data Definition Specification (Define-XML) for the metadata that accompanies SEND, SDTM, and ADaM datasets
- The FDA is supporting efforts to develop clinical terminology standards for therapeutic areas within the SDTM.SDTM will be updated periodically to include new and revised standards for specific therapeutic areas
For effective CDISC format and CDISC data standards legacy conversions and study data analysis, Freyr helps organizations navigate end-to-end publishing and submissions.
CDISC Legacy Data Conversions
- Enhanced innovation
- Facilitated data sharing for legacy data conversions
- Maximizing the value of clinical study data
- Complete traceability
- Improved data quality
- Streamlined processes
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- Fostered efficiency
- Faster FDA submissions
- Reducing the CDISC legacy data conversions-cycle-times and costs
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