anayansi gamboa standards

CDISC/CDASH Standards at your Fingertips

A standard database structure using CDISC (Clinical Data Interchange Standards Consortium) and CDASH (Clinical Data Acquisition Standards Harmonization) standards can facilitate the collection, exchange, reporting, and submission of clinical data to the FDA and EMEA. CDISC and CDASH standards provide reusability and scalability to EDC (electronic data capture) trials.

There are some defiance in implementing CDISC in EDC CDMS:

1. Key personnel in companies must be committed to implementing the CDISC/CDASH standards.

2. There is an initial cost for deployment of new technology: SDTM Data Translation Software, Data Storage and Hosting, Data Distribution and Reporting Software.

3. It can be difficult to understand and interpret complex SDTM Metadata concepts and the different implementation guides.

4. Deciding at what point in a study to apply the standards can be challenging: in the study design process, during data collection within the CDMS [CDASH via EDC tools], in SAS prior to report generation [ADaM], or after study completion prior to submission [SDTM].

5. Data management staff [CDM, clinical programmers], biostatisticians, and clinical monitors may find it difficult to converge on a new standard when designing standard libraries and processes.

6. Implementing new standards involves reorganizing the operations of (an organization) so as to improve efficiency [processes and SOPs].

7. Members of Data Management team must be retrained on the use of new software and CDISC/CDASH standards.

standards8. There are technical obstacles related to implementation in several EDC systems, including 8 character limitations [SAS] on numerous variables, determining when to use supplemental qualifiers versus creating new domains, and creating vertical data structure.

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Anayansi Gamboa, MPM, an EDC Developer Consultant and clinical programmer for the Pharmaceutical and Biotech industry with more than 13 years of experience.

Available for short-term contracts or ad-hoc requests. See my specialties section (Oracle, SQL Server, EDC Inform, EDC Rave, OpenClinica, SAS and other CDM tools)

As the 3 C’s of life states: Choices, Chances and Changes- you must make a choice to take a chance or your life will never change. I continually seek to implement means of improving processes to reduce cycle time and decrease work effort.

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11 thoughts on “CDISC/CDASH Standards at your Fingertips”

  1. Hi Ana. I am new to this CDISC standards stuff. What advantages it brings to the Pharmaceutical Industry?

    1. Generally speaking, the amount of time programmers spend with non-programming tasks (content, data structure review, output/formats, etc) is reduced with CDISC standards implementation. Most of the work is now been performed during eCRFs design/study build activities then SAS programmers or statistical team can generate the tables listings with minimal modifications.

  2. Hi Anayansi, I am a SAS programmer in India. What are the challenges as SAS programmer you think you will face when you first implement CDISC standards?

    1. With these requirements from updating electronic submission and CDISC implementation, understanding only SAS may not be good enough to fulfill the final deliverable. Statistical programming may need to start from common ground. All existing programs/macros may also need to be remapped based on CDISC.
      Good luck, Chetan

  3. CDISC SDTM Implementation Guide provides specifications for 30 domains. The SDTM domains are divided into six classes.

    The 21 clinical data domains are contained in three of these classes:
    Interventions,
    Events and
    Findings.

    The trial design class contains seven domains and the special-purpose class contains two domains (Demographics and Comments).

    The trial design domains provide the reviewer with information on the criteria, structure and scheduled events of a clinical trail. The only required domain is demographics.

    There are two other special purpose relationship data sets, the Supplemental Qualifiers (SUPPQUAL) data set and the Relate Records (RELREC) data set. SUPPQUAL is a highly normalized data set that allows you to store virtually any type of information related to one of the domain data sets. SUPPQUAL domain also accommodates variables longer than 200, the first 200 characters should be stored in the domain variable and the remaining should be stored in it.

    1. Hi Sara,

      First compare the metadata with the SDTM domain metadata. Assuming the data from the data management is in compliance to SDTM metadata, use automated mapping as your first step.

      If the datamanagement metadata is not in compliance with SDTM then avoid auto mapping. Map each variable to appropriate domain manually.

      Cheers, {EDC}

  4. Hi Ana, Nice article. I started reading more about CDISC lately. Could you give some examples of MAPPING?

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