Stay up to date with the latest clinical trials news and developments.
Clinical Trials Day 2023: 276 years on from the first clinical trial
Did you know that the first randomized clinical study was conducted back in 1747 by Scottish doctor, James Lind? Lind determined through an experiment conducted onboard a naval ship that oranges and lemons could be used to cure scurvy in sailors.
You’re invited to our ryze community event in Boston this May!
📅 Wednesday 24th May 2023
Easily validate clinical study data with Formedix CORE - download now!
We’re excited to announce the launch of Formedix CORE, the first free-to-use, downloadable application encompassing the CDISC Open Rules Engine (CORE).
See you in Gothenburg for the SCDM 2023 EMEA conference! | Formedix
📅26th – 28th April 2023
How clinical trial software can optimize clinical trials | Formedix
What is clinical trial software?
Clinical trial software is used by clinical research organizations (CROs), biotechnology, and pharmaceutical companies to facilitate clinical trials from conception to finish.
For example, specialized clinical trial software can help with:
* Protocol management
* CRF design
* Metadata management
* Collection and analysis of data
* Submission of compliant clinical study data to regulatory authorities
Specialized software can help you run clinical trials more efficiently and help you to get quality clinical products to the market faster.
Research organizations are faced with the pressures of study deadlines and a need to stay compliant with regulatory standards. And often, they’re running multiple clinical trials simultaneously. They’ve got to be efficient. They need to be able to see and manage their clinical trials effectively so they can identify process inefficiencies. They also need full transparency of the process from start to finish so they can report on this come submission time. And importantly, they need to collect the required data quickly and accurately.
This is why more and more organizations are engaging the help of clinical trial software companies offering cloud-based solutions.
Where is the industry now with clinical trial software?
The pharmaceutical industry has been slow to try new approaches and emerging software solutions. They’ve been focussed on getting clinical products to the market, despite process inefficiencies.
Traditionally, spreadsheets have been used to record and manage the various aspects of clinical trials, including data collection, study protocol and specifications.
But these traditional ‘manual’ solutions carry a high risk of errors, incomplete data collection, and process bottlenecks. As a result, efficiency, compliance, and patient care are often compromised.
The industry is now recognizing that to stay ahead of competitors and overcome tight timescales, technological cloud-based clinical trial software solutions are key for faster, more efficient clinical trials. The FDA encourages the use of cloud-based solutions to streamline the clinical trial process. The end goal is to have more effective medications and more personalized healthcare.
What solutions do clinical trial software companies offer?
Clinical trial software companies provide different types of software for different stages in the clinical trial process. There are four main types of software to help build and run clinical trials:
* Clinical Trial Management systems (CTMS)
* Electronic Data Capture system (EDC)
* Clinical metadata repository (CMDR)
* Clinical trial automation software
Below, we explore each type of software in more detail.
1) Clinical trial management systems
A clinical trial management system is an integrated cloud-based software platform that’s used for the end-to-end management of clinical trials.
A clinical trial management system is used to:
* Plan, track, and analyze clinical trials
* Find and manage participating patient
* Track participants’ involvement in clinical trials
* Manage finances
This software can help you improve the quality of your clinical products, reduce the time it takes to get a product to market and ensure compliance with industry standards and regulations.
Clinical trial management systems are often used in conjunction with other clinical trial software that specializes in a specific area, such as EDCs and integrated clinical study automation software.
2) Electronic data capture systems
An EDC is an electronic system used to gather patient data during clinical trials. EDCs typically have a user interface for users to enter data into electronic forms. A validation function will check forms have been filled in accurately and a reporting tool lets users analyze the data collected.
EDCs have been around since the 1990s and are improving all the time. Modern EDCs let you target specific patient profiles or study phases. Examples of modern features include cloud data storage, role-based permissions, CRF designers, clinical data analytics, interactive dashboards, and electronic health record integration.
Leading EDC companies
Using an EDC system can help you increase data accuracy, speed up the data collection process, and reduce costs over the lifetime of your clinical trial.
3) Clinical metadata repositories
A CMDR is a centralized location for you to store, manage and find all your study metadata, such as forms, standards and datasets, in one place. A bit like a library, or a single source of truth.
Metadata can be stored in various stages of development. It can be updated, approved, and kept as organizational standards. This gives you easy access to approved metadata you can reuse again and again. This means less time spent creating and approving metadata with every study.
Examples of standardized metadata include case report forms (CRFs), terminologies, datasets, and mappings.
A CMDR can help you with:
* CRF design
* Metadata management
* Standards governance
* Data warehousing
* Statistical computing
* Submission to regulatory authorities
What are the main features of a clinical metadata repository?
A good quality clinical metadata repository should have the following features built-in.
User access control
You should be able to control all internal and external user access and assign roles to each individual. For example, being able to assign view-only or edit access to different users. Ideally, it should be possible to set access by standard or study thereby allowing specific users to edit particular standards and studies, but not allowing them to edit others.
Impact analysis lets you see what upstream and downstream metadata and processes are affected if a particular change is made - before you make that change.
Global traceability and reporting show where standards and study content is being used. For example, you’ll be able to identify asset groups in other standards that use the same CRF, or SDTM datasets that are mapped to the CRF.
Impact analysis lets you make informed decisions on whether a proposed change is worth making, or not.
There should be a way for team members to request changes to existing metadata content. It must be possible to edit, add, and retire metadata content. The change management process should record what the change is, why it’s been requested, who made the change, who requested the change, when the change was requested, and when the change was made. These changes should go through a thorough governance process, like the example below.
Governance refers to the process that must be adhered to for any change to existing metadata content and organizational standards, or for the creation of new metadata content. A governance process, or workflow, must be built in to make sure that changes are managed and dealt with effectively in a way that suits an organization’s needs.
Being able to fully track metadata means that it’s easier to see how to improve metadata management. Having governance means there’s a fully traceable audit trail, the ability to do impact analysis, and the ability to see the flow of data through the system.
A quality clinical metadata repository should allow versioning of internal standards across the organization. It should let you update and improve both study level standards and organizational standards. For example, you might want to have various versions of the same CRF for different purposes. And changes to a version of a CRF will have an impact on SDTM mappings and TLFs (tables, listings, and figures).
Built-in compliance and validation
Compliance and validation ensure that a clinical study meets the expectations of regulatory authorities. It should be built in from the start of a study, through to submission. So, each part of the study should be tested against validation rules to make sure it stays compliant.
A clinical metadata repository should integrate with other systems. This could mean integration with an organization’s systems to allow them to upload data. Or it could mean integration with a SAS based system for pushing and pulling data in different formats, or an EDC system.
The benefits of using a CMDR
The key drivers for using a clinical metadata repository are:
* Regulatory compliance
* High data quality
* Process efficiency
* Save time and resources
* Get to submission faster
And many of these benefits come as a direct result of automation.
4) Clinical trial automation software
Automated processes and clinical metadata repositories go hand in hand. You’ll find that if you implement a CMDR, you’ll also be able to automate traditionally manual processes.
A clinical metadata repository stores all your organizational standards in one place. This means your content is easily accessible and ready to use across all your studies. A good CMDR also enables auto-generation of study artifacts such as EDC, SDTM, and ADaM specifications.
Automation simplifies processes for clinical studies, from study setup through to submission. It helps to accelerate studies and to reduce human error by removing manual processes. It also makes it easier for companies to comply with regulatory standards because submissions can be easily built to a compliant specification and data quality will be high.
By using a CMDR or other clinical trial automation software, you’ll be able to:
* Get clinical trials done faster
* See improved data quality and consistency
* Analyze data more quickly and effectively
* Reduce overall costs of the study
Read more about automation in our blog Automating clinical trials: Why it’s essential for success.
ryze CMDR and clinical trial automation software
Our off-the-shelf clinical trial automation software and clinical metadata repository is a fully integrated online platform for facilitating clinical trials.
ryze lets you store metadata content and build studies quickly using automated workflows. It simplifies the study design process and there’s no requirement for programming skills.
Our clinical metadata repository has all the desired features such as impact analysis, change management, and governance. And it has the necessary automated processes needed to generate the study artifacts required to make a submission to the FDA.
* Store and manage metadata, across the end-to-end life cycle of your studies
* Create metadata content from scratch, or upload your existing organizational standards
* Manage your organizational standards, helping to increase data quality while decreasing downstream costs
* Make validated CRF designs, EDC designs, and builds
* Convert data to produce validated SDTM datasets
* Create valid SDTM and ADaM define.xml files for submission
* Use the APIs to integrate with external systems
Automated processes in ryze
CRF design and visualizations
ryze makes it quick and easy to make all the different metadata formats you need. You can see how your CRFs will look and how they’ll work in your EDC system before you build it.
Generating annotated CRFs and blankcrf.pdf
Once you have your CRF designs in ryze, it’s easy to add annotations. Or if you have your CRFs standardized with annotations, that’s even better! You can just reuse your annotations.
If you need to make changes, you can instantly preview them. Then when you’re happy, it’s just one click to get your submission ready annotated CRFs in PDF format.
Read more about automating CRF annotations in our blog: Why you should switch to automated CRF annotations.
Generate SDTM datasets from source data
Start by defining the mappings from your source data to your SDTM. Then you can pull data directly from your chosen EDC system to generate your SDTM datasets after you start collecting data.
ryze supports all versions of CDISC standards and SDTM automation. We keep our platform updated in line with CDISC and NCI standards. That way your study designs and datasets are always regulatory compliant.
Generate SDTM and ADaM define.xml
Once you’ve built your study and defined your datasets, it’s just one click to generate your SDTM define.xml, and another click to generate your ADaM define.xml.
You can even generate define.xml from SAS XPT files or old legacy datasets. Learn more about our visual define.xml editor.
ryze lets you integrate with the leading EDCs. You can design your studies with all the features of the EDC you work with. You can see what your forms look like and how they’ll work in your chosen EDC as you design them. When everything’s finished, automatically build the EDC database with just one click.
You can also use our API to integrate with your own internal systems. That means you can set up automatic processes to push source data into ryze and trigger a conversion. Then, pull the datasets back into your system from ryze. You can also pull metadata in ODM and Define-XML formats.
Want to find out more about CMDRs and clinical trial automation?
Why not sign up for a free 30-day trial of ryze? You'll also get some training to help you get the most out of your trial.
Author's note: this blog post was originally published in August 2020 and has been updated for accuracy and comprehensiveness.
We'll be at CDISC Europe Interchange 2023!
📅26th – 27th April 2023
📍Tivoli Hotel & Congress Center, Copenhagen
The 2023 CDISC Europe Interchange will be an excellent opportunity to learn about the future of clinical data standards, industry progress, challenges and strategy.
Our team of friendly experts will be at booth #3 - drop by to learn how the ryze clinical metadata repository and automation platform can help you build high quality studies and get products to market faster.
And on day two of the conference, don't miss our presentation on CDISC's Open Rules Engine (CORE) and the launch of Formedix CORE ⬇️
How the new CDISC Open Rules Engine can improve the speed and quality of dataset deliverables in clinical trials
📅Thursday 27th April, 12:00 – 12:20pm
📍Vandsalen, Tivoli Hotel & Congress Center
Since our inception, Formedix has been focussed on automating study setup and downstream data conversions, whilst fully leveraging CDISC standards. The CDISC CORE engine takes this to another level - particularly during mid-study SDTM conversions. Now the engine can be connected with SDTM automation tools, providing in-stream quality checks as conversions are performed.
We'll explore how, together with the benefits of modern study build technology, CDISC CORE will help get therapies to market faster than ever before.
Easily run validations with Formedix CORE app for CDISC Open Rules
We'll also be showcasing the development and release of a preview version of Formedix CORE, which is a free-to-use desktop application, incorporating the CORE engine.
Formedix CORE is a free, downloadable Windows desktop application that allows users to run the CORE engine on their local desktop. The application provides an easy way to run validations on local data and produce validation reports. The reports can be used to identify points where the data doesn't comply with chosen standards, resulting in better visibility and data quality.
We'll consider the lessons learned in developing a free desktop tool using CDISC’s documentation and GitHub materials.
Finally, we'll discuss our plans to integrate this technology with our metadata repository and automation software, ryze, and the benefits this signifies for sponsors.
Mark your calendar!
Learn about faster and easier study setup with a ryze taster demo
If you’re interested in learning about quicker and easier study build, come along to booth #3 for a taster demo of ryze clinical metadata repository and automation platform?
In just 15-minutes, you’ll learn how to:
* Search and find all your metadata in one place
* Establish standardized metadata for leading EDCs
* Reuse content across standards and studies
* Comply with CDISC content standards required by the FDA
* See how eCRFs look and work for your chosen EDC as you design forms in ryze
* Build your EDC from ryze with one click
* Track changes, manage versions and see impact analysis
* Export all the metadata formats and dataset deliverables you need
Look out for our team!
Join us at ACDM 23 in Barcelona this March! | Formedix
📅 12th - 14th March 2023 | Booth #7
We’ll be at PHUSE US Connect 2023! | Formedix
📅 5-8th March 2023
What are SDTM supplemental qualifiers? | Formedix
We touched on the SDTM supplemental qualifier in the SDTM mapping process simplified. In this blog, we’ll delve deeper into this subject with detailed examples.
So, what are SDTM supplemental qualifiers? In short, these are variables in non-CDISC datasets that cannot be mapped to a variable that matches the SDTM standard.
What to do with new variables
The Study Data Tabulation Model (SDTM) includes a rule that new variables cannot be added to a data domain. If a user has additional data for a domain which cannot be entered into the domain using the standard SDTM variables, then a supplemental qualifier dataset must be used. This is a separate dataset from the ’parent’ domain in question, and it has a vertical structure that allows the user to add supplemental data in a ’variable name – variable value’ format.
Supplemental qualifiers example
Below is an SDTM+ dataset for DM (Demographics).
In DM, we have the standard SDTM data:
* STUDYID – the Study ID
* DOMAIN – the dataset domain code
* USUBJID – the unique subject identifier
* AGE, SEX, RACE – the patient’s age, sex and race
In the above example, we also have two variables that aren’t included in the SDTM – ITTFL and PPROTFL. These are the population flags Intent to Treat and Per Protocol.
In this instance, we create a supplemental qualifier dataset, SUPPDM, which is shown below. The naming convention for supplemental qualifier datasets must be adhered to. It will always begin with ’SUPP’ and is followed by two characters that represent the SDTM domain they were created for. So, as in this example, the supplemental qualifier for DM is SUPPDM. Creating a supplemental qualifier dataset for the domain EX (exposure) would result in SUPPEX.
Each supplemental qualifier dataset contains ten variables. Variables in a supplemental qualifier domain are either ’required’ or ’expected’. There are five key variables that reference a specific record in its parent domain and five Q-variables that contain the supplemental data itself.
The key variables are:
* STUDYID –the Study ID
* RDOMAIN – related domain
* USUBJID – the unique subject identifier
* IDVAR – variable which identifies the related records (usually the Sequence variable)
* IDVARVAL – the value of IDVAR (in SUPPDM, IDVAR and IDVARVAL are blank – the SDTM dataset DM contains only one observation per subject and USUBJID is sufficient enough to reference the records)
The supplemental data is:
* QNAM – the variable name
* QLABEL – the variable label
* QVAL – the data value
* QORIG – the origin (CRF/derived, etc)
* QEVAL – the evaluator
Each domain that has non-SDTM standard variables needs a supplemental qualifier dataset. So, if there are 10 datasets that contain non-SDTM standard variables, 10 supplemental qualifier datasets would be created.
What to do about NCI preferred terms for race
The NCI preferred terms for reporting the race of a patient only include American Indian or Alaska Native, Asian, African American, Native Hawaiian or Other Pacific Islander, or White.
So, what happens in a situation where a patient is, for example, Australian Aborigine? The SDTM Implementation Guide answers this question perfectly with the following example.
DM - Multiple Race Choices
In this example, the subject is permitted to check all applicable races.
* Row 1 (DM) and Row 1 (SUPPDM): Subject 001 checked ’Other, Specify’ and entered ’Brazilian’ as race
* Row 2 (DM) and Row 2, 3, 4, 5 (SUPPDM): Subject 002 checked 3 races, including an ’Other, Specify’ value. The three values are reported in SUPPDM using QNAM values RACE1 – RACE3. The specified information describing other race is submitted in the same manner as to subject 001
* Row 3 (DM): Subject 003 refused to provide information on race
* Row 4 (DM): Subject 004 checked 'Asian' as their only race
In the SDTM dataset dm.xpt, we see the demographics of Patients 001 – 004.
Here, Patient 001 chose ’Other’. Because his specified race cannot be matched to SDTM, a supplemental qualifier is created where the additional data is stored. So, we can see from suppdm.xpt that the patient has specified ’Brazilian’.
Patient 002 has a much more diverse background. They ticked ’Black or African American’, ’American Indian or Alaska Native’, and ’Other’. And in the ’Other’ field, they have specified ’Aborigine’.
Patient 003 has not answered the question, and Patient 004 has chosen the SDTM-compliant response ’Asian’, so their information is not included in the supplemental qualifier.
Creating SUPPQUAL datasets can be challenging and time-consuming. But, if you have datasets containing variables that cannot be mapped to standard SDTM variables and want to make them SDTM-compliant, it’s completely necessary!
Need help with CDISC SDTM mapping?
Download our free guide to SDTM mapping at the link below.
Author's note: this blog post was originally published in January 2021 and has been updated for accuracy and comprehensiveness.
A guide to CDISC standards used in clinical research | Formedix
The Clinical Data Interchange Standards Consortium (CDISC) is dedicated to helping improve medical research through data standardization. CDISC has worked closely with the United States Food and Drug Administration (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, for less cost.
There are many more benefits too:
* Increased efficiency
* Full traceability in the clinical research process from start to end
* Greater innovation
* Better data quality
* Easier sharing of data
* Cost reduction
* Streamlined processes
In this blog, we break down the CDISC standards you need to know about, what they are for, and why they’re important.
Content and Data Exchange standards
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
* Data exchange standards
CDISC Content standards
Content standards define what objects are allowed. For example, content standards might define an Adverse Events dataset and the variables it contains.
What content standards should you be aware of?
The content standards used in the clinical research process are:
We discuss each of these standards in more detail below.
The Protocol Representation Model (PRM)
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.
Clinical Data Acquisition Standards Harmonization (CDASH)
CDASH defines the best way to structure your 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.
Access CDISC’s eCRF portal, which contains ‘ready-to-use, CDASH-compliant annotated eCRFs, available in PDF, HTML and XML, to use as is or import to an EDC system for customization’.
Did you know that CDISC uses the Formedix clinical metadata repository to design and store their eCRF standards?
Find out more here >
Study Data Tabulation Model (CDISC SDTM)
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.
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 latest versions are:
* SDTM v1.7 which supports SDTM-IG v3.3
* SDTMIG-MD (Medical Devices) v1.1
* SDTMIG-PGx (Pharmacogenomics, Pharmacogenetics) v1.0
* SDTMIG-AP (Associated Persons) v1.0
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.
Wondering about SDTM mapping? Download our free best practice guide to SDTM mapping. Or, if you want to dig a bit deeper into SDTM, check out our blogs on SDTM supplemental qualifiers and LOINC codes for SDTM.
Standard for Exchange of Non-Clinical Data (CDISC SEND)
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 Analysis Dataset Model (CDISC ADaM)
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 also the related Analysis Results Metadata standard.
The characteristics of the CDISC ADaM standard are as follows:
* It’s submitted using Define-XML
* It’s more flexible than SDTM
* Traceability is built-in among analysis results, analysis data, and data represented in the SDTM
* It fulfils most data analysis requirements
Like SDTM, the ADaM standard also has an implementation guide.
The latest versions are:
* ADaM v3.1
* ADaM-IG v1.3
Read our blog on 3 things you should know about ADaM standards.
The diagram below shows an example of the different types of CDISC ADaM-related content that contribute to a final ADaM submission.
Questions, Ratings, and Scales (QRS)
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.
Some examples of published supplements include the 6-minute walking scale, Hamilton anxiety rating scale, and the Neuropathic pain scale.
Controlled Terminology (CT) 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.
You can read more on NCI CT in our blog about using NCI controlled terminology for standardizing data.
Therapeutic Area Standards (TA)
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.
CDISC Data exchange standards
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.
The latest version is ODM-XML v1.3.2.
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 the 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.
Define-XML 2.1 is the current version of the standard.
Read more about using Define-XML for dataset design and how to describe multiple origins for a value in Define 2.0.
Analysis Results Metadata (ARM)
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.
Laboratory Data Model (LAB)
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.
What CDISC standards are required for submission?
The FDA and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) require the following standards:
* SEND (Standard for the Exchange of Nonclinical Data)
* SDTM (Study Data Tabulation Model)
* ADaM (Analysis Data Model)
* Controlled Terminology
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.
Hear about the library from CDISC themselves by listening to a recent webinar we ran in collaboration with The University of Alabama, The University of Utah and CDISC entitled: the need for standardization and CDISC.
Need help with CDISC standards?
Formedix have been strong advocates for the use of CDISC data standards in clinical and non-clinical research and are industry leaders in CDISC-compliant software. In fact, we were one of the first ever CDISC members over 18 years ago. Today, we’re active members of the CDISC XML Technology team, and our friendly experts regularly speak at industry events.
And because ryze, our clinical MDR and clinical trial automation suite, is built on the latest CDISC standards, it’s safe to say we know our stuff!
To learn more about the CDISC standards required for regulatory submission, download our free guide by clicking the button below.
Author's note: this blog post was originally published in January 2021 and has been updated for accuracy and comprehensiveness.