The Pharmaceutical Industry is well-known for it's different mergers and acquisitions. These changes over the period of time have led to the radical changes in the way the clinical data is collected, analyzed and submitted.
This takes a toll on the SAS Programmers and Statisticians as they have to keep changing their process of data analysis according to the policies brought out by the merging and acquisitioning organizations. This mainly affects the long-term studies as they will have to manually redo all of the past analysis as per the new standards.
The different processes followed for the collection and submission of clinical data is harder on the regulatory bodies like FDA, PMDA etc., as for instance, an organization would code the gender value, Male as 'M', whereas another organization would tag the same as '0' or small m ('m') or any other code as per their internal standards. These differences delayed the drug approval process and thereby causing a delay in delivering life saving drugs to the patients.
In order to accelerate the drug approval process, the U.S Food and Drug Administration decided to implement a universal standard across all pharmaceutical organization.
CDISC was founded by Dr. Kush in 1997. She has over 40 years of experience in medical research and related process improvement, technology and standards. CDISC began as an all-volunteer organization with no funding, which later on grew to over 400 member organizations, an annual revenue of $7.5 million from diverse sources, and thousands of volunteers.
Now, U.S. (FDA), Japan (PMDA) and the European Medicines Agency (EMA) require CDISC standards to be adopted in the clinical trial applications.
CDISC Standards in the Clinical Research Process
SEND stands for “Standard for the Exchange of Nonclinical Data.” SEND guides the organization, structure, and format of all nonclinical data. The SEND Implementation Guide (SEND-IG) provides predefined domains and examples of nonclinical (animal) data based on the structure and metadata defined by the SDTM.
PRM stands for "Protocol Representation Model" provides a standard for planning and designing a research protocol with focus on study characteristics such as study design, eligibility criteria, and requirements from the ClinicalTrials.gov, World Health Organization (WHO) registries, and EudraCT registries. PRM assists in automating CRF creation and EHR configuration to support clinical research and data sharing.
CDASH stands for "Clinical Data Acquisition Standards Harmonization". CDASH establishes a standard way to collect data consistently across studies and sponsors so that data collection formats and structures provide clear traceability of submission data into the Study Data Tabulation Model (SDTM), delivering more transparency to regulators and others who conduct data review.
SDTM stands for “Study Data Tabulation Model.” SDTM is arguably the most well recognized and widely implemented CDISC standard. SDTM outlines a universal standard for how to structure and build content for data sets for individual clinical study data.
ADaM stands for “Analysis Data Model.” ADaM can also be thought of as data that is “analysis ready.” The main difference between ADaM and SDTM standards is the way in which the data is displayed. ADaM datasets can be used by the FDA to easily recreate analyses.
The above mentioned standards are termed as "Foundational Standards".
CDISC Foundational Standards are the basis of the complete suite of standards, supporting clinical and non-clinical research processes from end to end. Foundational Standards focus on the core principles for defining data standards and include models, domains and specifications for data representation.
CDISC Data Exchange Standards facilitate the sharing of structured data across different information systems. These include:
ODM-XML facilitates the regulatory-compliant acquisition, archival and exchange of metadata and data. It has become the language of choice for representing case report form content in many electronic data capture (EDC) tools.
Define-XML transmits metadata that describes any tabular dataset structure. When used with the CDISC Foundational Standards, it provides the metadata for human and animal model datasets using the SDTM and/or SEND standards and analysis datasets using ADaM.
Dataset-XML supports exchanging tabular data in clinical research applications using ODM-based XML technologies, enabling the communication of study datasets for regulatory submissions.
RDF provides a representation of the CDISC Foundational standards in a model based on the Resource Description Framework (RDF). RDF provides executable, machine-readable CDISC standards from CDISC Library.
CDISC Therapeutic Areas (TA) Standards extend the Foundational Standards to represent data that pertains to specific disease areas. TA Standards include disease-specific metadata, examples and guidance on implementing CDISC standards.
CDISC Controlled Terminology (CT) is the set of CDISC-developed or CDISC-adopted standard expressions (values) used with data items within CDISC-defined datasets. CDISC, in collaboration with the National Cancer Institute's Enterprise Vocabulary Services (EVS), supports the controlled terminology needs of CDISC Foundational and Therapeutic Area Standards.
CDISC Library
CDISC library is a central repository for developing, integrating, and accessing CDISC metadata standards. In other words, it’s an online electronic source for the CDISC content standards, allowing them to be viewed in a machine-readable way. It makes it easier for users to implement CDISC standards via clinical trial software such as a CTMS (clinical trial management system). CDISC library’s standards help to gather, aggregate, and analyze standardized data from early design through to end analysis.
It provides an API that helps to automate the implementation of CDISC standards. Users can access standards in real-time in a number of different formats. For example, RDF, XML, JSON, and CSV.
Benefits of Implementing CDISC Standards
CDISC is driven by the belief that the true measure of data is the impact it has, but for far too long, its full potential wasn’t being realized. So, CDISC enables the accessibility, interoperability, and reusability of data, helping the entire field of clinical research tap into—and amplify—its full value. From greater efficiency to unprecedented discoveries, CDISC makes it possible to turn information into invaluable impact for clinical research and global health.
The key advantages to implementing CDISC standards include:
Fostered efficiency
Complete traceability
Enhanced innovation
Improved data quality
Facilitated data sharing
Reduced costs
Increased predictability
Streamlined processes
CDISC has established five high-level goals to help guide this organization, and the global community it supports, as follows:
I. Transform. Transition to a multidimensional representation of CDISC standards and support automation.
II. Expand. Identify and prioritize adjacent research areas that can benefit from data standardization.
III. Support. Ensure that a vibrant global community is heard and well-served.
IV. Include. Reduce the barriers to entry and use for those who utilize CDISC standards.
V. Engage. Raise awareness of the benefits of data standardization among key stakeholders.
CDISC also offers a variety of courses, workshops and certifications that can be accessed in the CDISC Learning System.
Stay tuned for more detailed information on the different CDISC standards in the upcoming posts.
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Sources
SAS Programming in the Pharmaceutical Industry, Second edition by Jack Shostak
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