The Clinical Data Interchange Standards Consortium (CDISC) is dedicated to helping improve medical research through data standardization. CDISC has worked closely with the FDA to introduce data standards, which make it easier for regulatory reviewers to understand and process clinical trial data. Standardized data has been a mandatory FDA requirement for all clinical studies submitted since December 2016.
Now that the pharmaceutical industry is working together using the same standards, clinical trials can be more easily optimized. Data standardization enables the rapid design, build, analysis, and submission of clinical trials. This means new treatments, procedures and tools get to market more quickly and for less cost.
There are many more benefits too:
In this blog, we break down the CDISC standards you need to know about, what they are for, and why they’re important.
CDISC standards cover everything from planning and data collection through to data analysis and reporting.
CDISC standards can be grouped into two different areas:
Content standards define what objects are allowed. For example, content standards might define an Adverse Events dataset and the variables it contains.
The content standards used in the clinical research process are:
We discuss each of these standards in more detail below.
The PRM is the very first stage in the end-to-end clinical trial planning process. It’s a conceptual model that’s used to organize the protocol. It identifies items in the protocol and organizes them into a machine-readable structure.
According to CDISC, ’the Protocol Representation Model (PRM) 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.’
PRM is not a deliverable and it’s not as commonly used as other CDISC standards such as SDTM and ADaM.
CDASH defines the best way to structure your case report forms (CRFs) to ensure you gather all the relevant data you need for commonly used domains.
CDASH standards help to improve data quality, reduce data queries, and make it easier and more efficient to do the SDTM mappings required for regulatory submission.
SDTM was developed to organize data collected in human and animal clinical trials. Adhering to SDTM standards helps provide a clear description of the structure, attributes, and content of each dataset, as well as the variables submitted as part of a clinical trial. SDTM metadata is submitted to regulators using the Define-XML data exchange standard.
SDTM was developed so that clinical trial submissions could be standardized and streamlined. Before SDTM, clinical trials differed vastly from each other. For example, there were differences between domain names, variables used, and variable names. This made the review process overly complex and time-consuming. The end result was that it took far longer to get a drug to market.
Note
CDISC SDTM consists of 2 parts, the underlying Study Data Tabulation Model and Implementation Guides (SDTM-IGs) that define how the SDTM should be used to represent some common data domains in human clinical trials.
The core model provides a standardized set of variables, which are grouped into ’classes’. These are refined and built into domains, for example Vital Signs. The implementation guides serve as a guide for implementing the CDISC SDTM standard.
The diagram below shows an example of the different types of CDISC SDTM-related content that contribute to a final SDTM submission.
QRS (Questionnaires), TA (Trial Arms), and CT (Controlled Terminology) in the diagram above are covered later in this article.
SEND standardizes the exchange of non-clinical data between systems in a consistent format. SEND metadata is submitted to regulators using the Define-XML data exchange standard. It’s required by the FDA for submissions. It’s an implementation of the SDTM standard that’s used for animal studies. In other words, data that is collected for animal studies can differ from data that is collected for humans, and the SEND standard attempts to fill this gap.
The CDISC ADaM standard was developed to standardize analysis data. It ties in very closely with SDTM. While SDTM is for collected data, ADaM is for presenting analysis data. ADaM datasets must always be derived from SDTM datasets. ADaM metadata is submitted to regulators using the Define-XML data exchange standard, and the related Analysis Results Metadata standard.
The characteristics of the CDISC ADaM standard are as follows:
Like SDTM, the ADaM standard also has an implementation guide. Keep up with the latest versions here.
The diagram below shows an example of the different types of CDISC ADaM-related content that contribute to a final ADaM submission.
QRS instruments are questions, tasks, or assessments for qualitative and quantitative assessment for clinical trials. A questionnaire is a series of related questions that produce one or more scores. Ratings are a ranking of quality, standard, or performance. Scales are defined based on criteria that result in a single measurement.
The QRS team develops Controlled Terminology and SDTM supplements, while the ADQRS team develops ADaM supplements. CDISC creates supplements for questionnaires, functional tests, and clinical classifications. These supplements provide standards for collecting and storing responses from QRS.
CDISC partnered up with the National Cancer Institute (NCI) to publish an evolving set of terminology standards. These are used along with content standards like SDTM to help ensure that the content of the data is easy to understand and is consistent.
Controlled terminology is a set of code lists and valid values that are references by questions in forms and variables in datasets. In a nutshell, it tells you how you should submit collected data for a data item in a dataset. For example, if a question on the CRF is to record sex, the allowed values are F (female), M (male), U (unknown), or UNDIFFERENTIATED.
Controlled terminology is used for PRM, CDASH, SDTM, SEND, and ADaM standards.
Therapeutic area standards are an extension of the foundational standards. They serve to represent data for specific therapeutic areas such as asthma, diabetes, multiple sclerosis, and many more.
Therapeutic Area User Guides (TAUGs) are implementation guides for each specific area. The CDISC foundational standards define in general how each different type of data should be submitted, and the TAUGs provide more specific information about how to interpret the foundational standards within a particular therapeutic area. For example, they might define how a variable should be interpreted in the context of a specific TA.
Data exchange standards are ways of representing metadata and data in a standardized way in order to make it easier to exchange data between different parties. They define how you describe an object.
Data exchange standards described in this article include:
We discuss each of these standards in more detail below.
The CDISC Operational Data Model (ODM) is an XML-based model for standardizing the transfer of metadata for clinical trials and the associated data. It can be used for defining the data collected in a trial, such as CRFs and patient diaries, to provide an upfront specification for the trial. This can then be used to help automate the build of the data collection systems. It can also be used for transferring the data itself once collected.
Some other models such as Define-XML, Dataset-XML, and Analysis Results Metadata are implemented as extensions to ODM-XML.
By using ODM and CDASH together, you can rapidly define the data you need to collect in your clinical trials. This ultimately helps to reduce the time it takes to get a drug to market.
Dataset-XML supports the exchange of tabular data using ODM based XML technologies. It allows communication of study datasets for regulatory submissions.
Define-XML is a data model that allows a standardized description of tabular dataset structures, helping to drive process efficiencies throughout the clinical lifecycle. Simply put, it describes the structure and content of data collected or submitted during the clinical trial process. This includes system-specific dataset structures that are exported from an EDC system and standardized CDISC domains such as SDTM.
It’s an extension of the CDISC ODM standard and is a key requirement for describing datasets that are to be electronically submitted to the FDA and PDMA.
CDISC standardized the description of ARM for describing Tables, Listings, and Figures (TLFs). This references the data in standardized ADaM datasets, making it easier to re-use analysis results metadata across different studies.
Lab was established to standardize the transfer of data between clinical laboratories and sponsor companies. The CDISC LAB model is widely used in pharmaceutical and biotechnology companies today.
It was developed because of the many variations between data acquired in laboratories, such as lab test names and units. So, by using the LAB model, laboratory data is standardized allowing seamless transfer between laboratories and CROs, which saves time and costs.
The FDA and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) require the following standards:
Additionally, CDISC standards are the preferred standards for electronic submissions to the Chinese National Medical Products Administration (NMPA).
The 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).
The library 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.
Our Clinical Metadata Repository can help you manage your standards and build your studies with greater speed and quality.