What is CDISC and how is it used in clinical data?

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Data Collection

As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies

In the realm of clinical research in today’s world, handling huge volumes of data from clinical trials plays an important role in ensuring analysis and making informed decisions based on it [1]. The data gathered by clinical trials come in complicated sets derived from EDC systems, laboratories, and patient records. It requires effective management that can be achieved only through data standardization.

To solve such problems, CDISC provides a standard format for managing clinical data for organizations involved in research.

What is CDISC?

Clinical Data Interchange Standards Consortium (CDISC) is an international body that creates standards for the collection, structuring, analysis, and submission of clinical research data. Such standards have been used extensively by many organizations within pharmaceutical, biotechnological, and healthcare sectors [2].

The use of CDISC-compliant datasets is mandated by regulatory agencies like FDA and EMA, which makes it important to have CDISC compliance for any clinical trials.

Significance of CDISC for Clinical Trials

CDISC is extremely important to maintain the standardization of data in clinical trials.

Advantages of CDISC Standards:

  • Facilitates creation of quality and consistent data sets
  • Supports integration of data among different applications
  • Saves time for regulatory submission process [3]
  • Prevents redundancy and errors in clinical trials data sets
  • Fosters collaboration in international clinical trials research

Organizations will benefit from adopting the CDISC standards.

CDISC Standards Used in Clinical Trials

1. CDASH (Clinical Data Acquisition Standards Harmonization)

Ensures accurate and consistent data acquisition processes within the trial sites.

2. SDTM (Study Data Tabulation Model)
Facilitates organization of raw data from trials for regulatory submissions and information exchanges.

3. ADaM (Analysis Data Model)

Aids in statistical analysis and reporting processes to enhance decision-making.

4. Define-XML

Provides metadata and dataset descriptions required for regulatory review and submission [4].

How CDISC is Used in Clinical Trials (Workflow)

1. Data Capture

CDASH provides consistent data collection during clinical trials.

2. Data Transformation

Raw data sets are transformed to SDTM standards for uniformity.

3. Data Processing

ADaM data sets are used for biostatistics processing.

4. Regulatory Approval

Consistent data sets are submitted for regulatory approval [5].

5. Data Interoperability

CDISC allows easy integration of clinical data into health care systems

Advantages of Using CDISC

  • Better quality of data
  • Exchange of standardized data between different systems
  • Increased speed of regulatory clearance
  • Cost savings during research process
  • Scalable solutions to manage bigger clinical trials [4]

These advantages will greatly improve the effectiveness of the clinical research process.

Without CDISC vs with CDISC

Aspect Without CDISC With CDISC
Data Format Non-standard and varied Consistent and standardized
Interoperability Complex Seamless
Regulatory Filing Lengthy and prone to errors Convenient and compliant
Data Integrity Higher probability of mistakes Avoidance of mistakes
Coordination Uncoordinated Coordinated globally
Market Launch Increased delays Rapid trial completion

Applications for CDISC in Clinical Research

CDISC standards are applied in many industries such as:

  • Pharmaceutical firms for drug discovery and development.
  • Contract research organizations for data management and analysis.
  • Biotech companies for R&D.
  • Regulatory authorities for data validation.
  • Academic institutions for conducting clinical research studies.

The above examples clearly show the need to use structured and standardized research data [3].

Challenges in CDISC Implementation

Despite its benefits, there could be some practical issues to consider:

  • Expensive deployment costs for tools and infrastructure
  • Training needs to familiarize teams with CDISC standards such as SDTM and ADaM
  • Difficulties in migrating to new systems
  • Lengthy data mapping and transformation processes
  • Complex integration with current healthcare IT systems
  • Shortage of skilled personnel

These potential problems can be mitigated through careful planning and expert consultation [4].

Future of CDISC in Clinical Research

With ongoing developments in clinical trials, CDISC is expected to facilitate innovation by:

  • Combining CDISC with artificial intelligence (AI) and machine learning (ML)
  • Using real-world data (RWD) to gain valuable information
  • Improving real-time data analysis capabilities
  • Incorporating digital health and decentralized trials

CDISC will play an important role in future healthcare innovations.

Conclusion

Clinical Data Interchange Standards Consortium (CDISC) is a vital part of clinical research that facilitates efficient and compliant data management. It increases efficiency, reduces delays, and improves cooperation [5].

Statswork offers professional data annotation services that help companies become CDISC-compliant, increase efficiency, and manage their research processes.

Reference

  1. Jentoft, K., Tustison, E., & Yu, H. (2023). CDISC Implementation in an Academic Research Organization. Journal of the Society for Clinical Data Management, 2(S1). https://www.jscdm.org/article
  2. Hume, S., Chow, A., Evans, J., Malfait, F., Chason, J., Wold, J. D., … & Becnel, L. B. (2018). CDISC SHARE, a global, cloud-based resource of machine-readable CDISC standards for clinical and translational research. AMIA Summits on Translational Science Proceedings, 2018, 94. https://pmc.ncbi.nlm.nih.gov
  3. Block, P. B. CDISC Clinical Research Glossary. https://www.proquest.com
  4. Baker, R. L., Hamidi, M., McKenzie, L., Denney, C. K., Edgar, T. D., Baker, R., & Edgar, T. (2023). The Story Behind the HL7 FHIR to CDISC Mapping Implementation Guide. Journal of the Society for Clinical Data Management, 4(1). https://www.jscdm.org/article
  5. Lee, A. J., Kim, K. W., Shin, Y., Lee, J., Park, H. J., Cho, Y. C., … & Yoon, B. S. (2021). CDISC-compliant clinical trial imaging management system with automatic verification and data transformation: focusing on tumor response assessment data in clinical trials. Journal of Biomedical Informatics, 117, 103782. https://www.sciencedirect.com