After about five years since the release of CDISC IG 3.4, CDISC has just released CDISC IG 4.0 and SDTM 3.0 for public review. Comments are due April 6, with expected final release expected later this year.

The public review includes the Conformance Rules version 3.0 as well as three draft Knowledge Base articles exploring some of the main changes expected with IG 4.0:

• NS– Datasets: Why they were built as they were.
• Why change the structure of SDTMIG metadata?
• Why does the DC domain differ from what’s described in FDA’s TCG?

For a quick overview of the impact of these changes, see the CDISC Standards timeline webpage or the revision history available in the draft version wiki for public review.

 

Celebrating or regretting the end of SUPP?

We will be moving, for example, from something called SUPPAE to something called NSAE, with a less “normalized” structure. Will this be “a small step for a man, a giant leap for mankind”? “Ai posteri l’ardua sentenza.”1

The change will require us to go from this:

to this:

The structure of these new dataset(s) is “One record per related dataset record,” meaning that the many-to-one relationship will no longer be possible, for example, an NS that applies to several records in the parent domain via –GRPID. That said, there is a hope that this new structure will simplify metadata handling and potentially facilitate the adoption of future data exchange format, such as CDISC Dataset-JSON.

 

Three new domains

Three new proposed domains have been introduced:

• DC (Demographics for Multiple Participations)
• GI (Gastrointestinal System Findings)
• EA (Event Adjudication) are three new proposed domains

DC has been around, unofficially, for some time, following the requirements introduced by the FDA in its FDA Study Technical Conformance Guide (see here my previous blog). This domain supports the representation of multiple enrollments within the same study. Along with DC, SUBJID has been added to all subject-level domains to differentiate data “generated” from each individual subjects’ participations.

Compared with FDA requirements, SDTM IG 4.0 also covers scenarios in which the same subject is enrolled multiple times, not only multiple screenings.

Identification of “Primary Enrollment,” and therefore how DM variables are populated, is left to the sponsor’s discretion. However, in cases where a subject experiences one or more screen failures before finally enrolling, the successful enrollment should clearly be considered the primary one.

EA, a Findings About domain, provides a common structure for studies requiring independent, peer-reviewed endpoint adjudication. In my view, it partially solves the issue of representing study endpoints where more complex “adjudication” is required; for example, in oncology study with efficacy based on tumor response.

 

Changes in metadata

Several new metadata have been introduced, along with some changes. The goal is to improve understanding of variables and their intended use, without impacting metadata included in a submission, e.g., define.xml.

So, when looking at the new SDTM IG, you will notice the following key differences among others:

• Controlled Terms, Codelist or Format is now split into three separate columns
• Variable Group has been added to group variables, for example Results Unit, or Results Value
• Some information previously included the “CDISC Notes” column are now reported in the “Examples” column

 

Other Changes

New versions of IGs are also an opportunity to fix issues (such as typos) and to clarify implementation that previously caused misunderstandings. For example, additional guidance on what Specimen-based Findings domain to use under specific circumstances, such as clarifying that anti-microbial antibody testing data should be mapped to IS domain rather than MS.

Some standard variables have been deprecated, such as –BLFL (Baseline Flag) for Findings domains, and others have been added. One notable addition is –CLASI variable, particularly useful for classifying Protocol Deviations to support requirements for “ICH E3 Q&As (R1)).” This variable is now officially part of the DV domain as DVCLASI, e.g., MINOR/MAJOR. More details on planned new and deprecated variables in all Observational Classes can be found in the CDISC Wiki.

Rumors about deprecating the PP domain appear to be unfounded, as PP is still there.

 

Want to know more?

You can participate in the public review and explore the details yourself. Check here.

My former colleague Varun Debbeti has also done an excellent job in his clinstandards webpage.

A more in-depth discussion of the expected changes will be also presented at upcoming CDISC-EU Interchange in May and this time in my hometown, Milan, and co-chaired by my colleague Silvia Faini.

Cytel will be present with two oral presentations and one poster:

• “It Got Worse Than Expected: Three Years of Retrospective CBER Requests on SDTM, ADaM, and TFLs” by Mark Malayas and Angelo Tinazzi
• “Authenticity Matters: Preserving Standards Integrity from Clinical Data Models to Tiramisù” by Angelo Tinazzi
• “JSON and CORE Unlocking Adoption” by Silvia Faini, Sebastià Barceló, Hugo Signol, and Angelo Tinazzi

See the here full draft agenda.

We look forward to reconnecting with colleagues from around the world, meeting new peers, and exchanging ideas at 2026 CDISC + TMF EU Interchange 2026.

See you in Milan?

Key takeaways from CDISC EU Interchange and PHUSE-CSS

As clinical data science evolves rapidly, the CDISC EU Interchange and PHUSE-CSS conferences offer a glimpse into the future of regulatory submissions, standardization, and the rise of open-source tools and AI in drug development. In May, I had the privilege of attending both the events, in Geneva and in Utrecht. I’d like to share here some highlights from both conferences.

 

Data submission in Europe: EMA delays

As anticipated in my previous blog, we were waiting for further announcements from the EMA regarding the outcome of their pilot raw data submission project, for which an interim report was published last year.

Those, like me, who were expecting a final announcement were likely disappointed. The requirement for data submission to the EMA in support of drug approval has been postponed to 2028. The EMA, which was well represented at PHUSE-CSS, needs to further evaluate factors such as tools and technological impacts more broadly. At PHUSE-CSS, they showed particular interest in topics such as Dataset-JSON, BIMO, the use of tools such as R-Shiny and the {teal} framework, as well more advanced topics still under development such as the “Analysis Concept.” The pilot continues, and the EMA is seeking more volunteers. It was guaranteed that submitting data will not negatively affect or delay your drug approval!

It was clear, while discussing with EMA representatives, that a number of stakeholders within the agency still need to be convinced of the benefits of receiving datasets in addition to PDF documents and reports. Some appeared concerned about the additional time and effort required to assess submitted datasets. As we all know, updating regulations, as well as releasing new standards, require a great deal of “diplomacy” and the consensus among multiple stakeholders.

 

Open source: {teal} and R-Shiny adoption

The “open source” revolution continues to gain its momentum in our industry. At PHUSE-CSS, I attended the “{teal} Success Stories” workshop, where various sponsors, including J&J, Sanofi, Novartis, Boehringer, and Roche, shared their experiences.

I was fascinated by the solutions those sponsors have already implemented using {teal}, and how straightforward it seems to develop R-Shiny applications using the framework provided by this R package, which was originally developed by Roche.

For a deeper insight into the capabilities of this package, I recommend reading the paper presented by Roche at PHUSE US 2024.

 

Dataset-JSON pilot update

Another interesting workshop I attended at PHUSE-CSS was on Dataset-JSON, where we reviewed and contributed to a consolidated set of comments and feedback in response to the “FDA Requests for Public Comment on CDISC Dataset-JSON Standard,” which closes next week on June 9, 2025.

While the benefits of such a standard were widely acknowledged, particularly in accelerating drug approval and improving overall interoperability, the discussions also highlighted potential risks and implementation challenges. These included concerns about numeric precision when importing Dataset-JSON to and from SAS, as well as handling special characters.

We therefore emphasized the need for the FDA to provide additional guidance to support future adoption; there was also interest in possible future extensions of Dataset-JSON, such as the inclusion of more metadata and the potential to embed define.xml.

 

BIMO

BIMO was the focus of another PHUSE-CSS workshop. Among all the various topics discussed, such as the presentation of the BIMO PHUSE template reviewer guide, it was particularly interesting to learn that PHUSE will soon be developing a dedicated FAQ to support sponsors and CROs on the gray area of the BIMO requirement such as the definition of “major” studies, currently the object of BIMO requirements.

 

CDISC 360i reboot: Toward an end-to-end digital pipeline

The CDISC 360 initiative is back, and stronger than before, with a major shift toward a fully digitalized and standard-driven clinical development lifecycle. The goal is to break down silos through the application of standards such as the USDM and Biomedical concept. The mission is ambitious, but unlike when CDISC 360 was first launched, we now have more mature standards and technology to support it.

 

Use of AI to support clinical standards

AI remains a hot topic, and as in 2024, a full session was dedicated to it at the CDISC EU Interchange. The common theme across most presentations was the use of generative AI to support the implementation of data standards, such as AI acting as a subject matter expert (SME) for study teams. Although many of the showcased solutions from Argenx, SGS, and AstraZeneca are still in beta, they clearly demonstrate how proper model training can enhance search and navigation within complex data standards libraries, or help manage complex, multidimensional data (e.g., omics, wearable biosensors). Other AI use cases were also featured in several posters at PHUSE-CSS; for example, the application of AI to generate synthetic data or automate local lab ranges.

 

Other topics

For topics such as Digital Data Flow and USDM, I’ll refer you to the LinkedIn newsletter “View From The Coffee Shop,” curated by my friend Dave Iberson-Hurst. In it, he regularly shares insightful thoughts and updates on the ongoing digitalization efforts within our industry. He also summarized some key takeaways from both the CDISC EU Interchange and PHUSE-CSS.

I also had the opportunity to see good use cases of Analysis Results Standards (ARS) at CDISC EU Interchange, showing this relatively new standard have been well perceived by sponsors as well as vendors.

On the regulatory side, aside from the news from the EMA, I found the presentation from Sanofi and GSK particularly interesting. It covered a cross-industry initiative aimed at harmonizing vaccine regulatory submission to FDA-CBER, by sharing experience with this unique division, which often has its own set of sometime unexpected requirements (see also my previous blog on submission experience with FDA-CBER).

For other topics, see also here official CDISC posts for other conference sessions content:

• Digital Data Flow
• Real World Evidence
• CDISC Open Rules

 

Ongoing innovation

Overall, both conferences continue to showcase ongoing innovation in our Industry. It’s clear that change is happening at a pace I have never seen before in my 30-year career, and that’s good for patients, as well as an exciting time for those of us working in biometrics.

 

Interested in learning more?

Download Angelo’s new ebook, The Good Data Submission Doctor on Data Submission and Data Integration to the FDA, a collection of Angelo’s most critical insights on clinical data standards submission to the FDA, including key updates from the new FDA Study Data Technical Conformance Guide:

This week, on May 14–15, the CDISC + TMF EU Interchange 2025 will take place, just a few steps from our Cytel’s Geneva office.

This year at Cytel, we’re making the event even more special! We will be hosting a pre-conference, open to anyone able to arrive into Geneva before the event begins.

But that’s not all, together with my Cytel colleagues we will have three presentations and one poster, where my colleagues will share insights from their work with CDISC standards, including the Trial Master File Standards Model (CDISC-TMF). And it is from the contribution of my colleague Caroline Terril that I’d like to start with some anticipations of what the four Cytel presentations will be about.

 

Key Considerations for Biometrics CROs Not Managing the TMF — The Journey So Far

Caroline Terril, Thursday, May 15, 10:00–10:30 — Session 5E: TMF Management (TMF Track)

If you’ve ever asked yourself after these last three years what really matters when it comes to managing the TMF with CDISC — especially for biometric CROs that don’t directly manage TMFs — then my colleague Caroline Terril might have the answer. In her presentation, she will delve into our journey so far in trying to adopt the CDISC-TMF standard.

 

The Curious Case of External Controlled Arms (ECA): Practical Solutions for External and RWD Integration

Gautham Selvaraj (co-authored by me), Wednesday, May 14, 09:30–10:00 — Session 5C: Real World Data

At Cytel, we’ve seen increasing use of external control arms (ECA) in sponsor projects. In this presentation, Gautham Selvaraj will walk through two real-world case studies on integrating ECA data into CDISC-compliant datasets, exploring the unique challenges and solutions in aligning real-world data with CDISC standards.

Interested in learning more about ECA clinical trial design? Explore more of Cytel’s offerings and insights:

• Early Planning Strategies for External Control Arms in HTA and Regulatory Submission
• Oncology Trials: External Control Arms and Bayesian Borrowing (Webinar)
• Cytel Solutions for Real-World Data in ECAs

 

Governing the Ungovernable: Can a CRO Effectively Govern Its Standards?

Angelo Tinazzi, Thursday, May 15, 14:30–15:00 — Session 7C: Applied Standard Governance

Are you a CRO struggling with different Sponsor Interpretation of Data Standards? Or perhaps across multiple therapeutic areas or indications? Hard life, isn’t it?

Spoiler alert: although there’s no “magic” in my presentation, and no AI involved, I will offer practical insights into the complexities of CRO data standard governance. Sponsors are also welcome to join to see what life looks like from the other side of the barricades!

 

Managing SDTM Mapping Challenges in Multi-Study Portfolios: A Guide to Standards and Consistency

Jing Zhang and Marianne Dutfoy, Wednesday, May 14, 12:30–13:30 — Poster Session

In their poster, Jing Zhang and Marianne Dutfoy offer guidance for navigating SDTM mapping across multi‐study portfolios. They’ll address challenges such as inconsistent CRFs, variations in source data, and the hurdles of aligning historical studies with newer versions of standards.

 

Interested in learning more?

Download Angelo Tinazzi’s new ebook, “The Good Data Submission Doctor on Data Submission and Data Integration to the FDA”:

This last year has been marked by new standards, industry templates and initiatives, and regulatory guidance — an intense year for Clinical Data Standards Development.

And it can certainly be challenging for sponsors to keep up with the evolving landscape: Standard Development Organizations (SDOs), such as the CDISC, advanced clinical data standards; regulatory agencies like the FDA added specific requirements; and industry initiatives such as PHUSE worked to clarify and address different stakeholders’ expectations through the development of white papers.

Here I provide a quick summary of what was released in the year we have just left behind.

 

Regulatory guidance

While the industry awaits the EMA’s long-anticipated clinical data standards submission requirements (see the current status of their pilot project here), the FDA celebrated the 10th anniversary of its Study Data Conformance Guide last April. If you missed it, check out my previous post “New FDA Data Submission Requirements and Substantial Changes,” which discussed a decade of guidance improvements and the latest requirements. Since then, the FDA has released two new versions of its Clinical Data Standards Guidance. These updates do not introduce major new requirements other than confirming that SDTM and ADaM Medical Devices IG for both CDER and CBER FDA divisions “align with FDA current business needs.”

It’s also worthwhile to mention the recent release of the FDA’s Protocol Deviations for Clinical Investigations of Drugs, Biological Products, and Devices. The guidance provides various recommendations around the management of protocol deviations, including some specific recommendations for SDTM mapping, such as including a variable in the DV domain that provides the sponsor’s determination of whether the protocol deviation was important. See also my friend Eanna Kiely’s LinkedIn post.  

 

CDISC standards

While continuing to advance initiatives aimed at full clinical trial study data digitalization with for example the release of the third version of the CDISC USDM (CDISC Unified Study Data Model), throughout 2024, CDISC continued to release new standards, update existing ones, and advance cross-industry initiatives such as CDISC CORE.

Notable examples of new releases in 2024 include the first version of the CDISC Analysis Results Standard, aimed at providing a framework for linking analysis results directly to the data and metadata that support them. Several presentations and workshops were held at various events over the past two years to promote first the idea and then gather feedback from the audience prior to its final release last April.

Later in the year, similar to the eCRF Portal, an eTFL Portal was also released. This resource includes examples of the most common analysis table layouts, complete with table shells, input ADaM datasets, define.xml, and the JSON version of the analysis results along with metadata. While not a regulatory requirement, this standard adds an important missing piece in our industry’s “dream” end-to-end goal.

It is also worth mentioning the first version of the CDISC Tobacco Implementation Guide (TIG), developed in collaboration with the FDA’s Center for Tobacco Products (CTP). This guide aims to assist tobacco companies in implementing CDISC foundational standards, such as CDASH, SDTM, and ADaM. If you are working at a Tobacco company, check also the recent launched TIG eSubmission Pilot.

Other highlights include updates to existing standards and new resources added to the CDISC Library (see the full list of resources here). Regular updates to CDISC Biomedical Concepts also became a key focus of the CDISC Library.

For a look ahead, check out the CDISC Standards Timeline for 2025.

 

Industry initiatives

PHUSE continued its work through dedicated Working Groups (WGs) focused on Optimizing the Use of Data Standards. In addition to maintaining a peer-reviewed QA for SDTM ADaM Implementation, these WGs have released two White Papers (WPs) summarizing the Data Standards challenges, nowadays and looking ahead with new trial designs, and how companies implement and govern Data Standards.

Both WPs are based on the outcomes from two surveys conducted during the 2022 and 2023 PHUSE Computational Science Symposium (CSS), with the second one in particular proposing some potential governance best practices across the industry aiming to establish some implementation consistency across companies. It’s worthwhile to mention that these WPs share that about 50% of respondents “affirmed” they do not have a dedicated data standards team or were unaware if one existed in their company. Among those with data standards teams in place, there was significant variation in structure, ranging from cross-functional groups to teams focused on specific functions. Only a few centralized teams addressed topics beyond foundational CDISC standards, such as SAPs and TFL generation. The survey also highlighted that many organizations, more than 50%, rely on Excel or internally developed tools for managing standards, as commercial metadata repositories (MDRs) are often seen as complex and require long-term investment. These challenges remain consistent across pharma, biotech, and CROs.

Applying CDISC standards to non-interventional studies and real-world data continues to be a challenge due to diverse data sources and scenarios, such as the use of external control arms. This was clear when I had the opportunity to host a CDISC ADaM training at one company in Europe specializing in Observational Studies.

The release of the CDISC Considerations for SDTM Implementation in Observational Studies and Real-World Data v1.0 in February addressed some of the SDTM mapping issues. Similarly for the previously released PHUSE Data Standards for Non-Interventional Studies, which covers additional ADaM-related topics, the FDA Data Standards for Drug and Biological Product Submissions Containing Real-World Data guidance (December 2023) provided some initial recommendations when submitting data packages including real-world data.

With the release of the first version of the CDISC Dataset-JSON in 2023, the long-awaited alternative to SAS XPT as a standard for data submission took a significant step forward. Following the release of the first version, in collaboration with the FDA and CIDSC, PHUSE completed a pilot project in 2024, with a full report made available last June. Last December, CDISC released an updated version (1.1) of the standard. Among recommendations from testers, some bugs were detected, for example, when importing or exporting to SAS, which includes some known differences between software such as SAS and R with numeric precision and date representation, or limitations requiring updates to analytics tools used by the FDA (e.g., SAS JMP). Details of the changes implemented or items rejected from the public review can be found here.

In 2024, open source continued to be a leitmotiv in our industry, with tools and initiatives aimed at regulating the use of open-source solutions in clinical data submission (see also Cytel blog “The Journey into Open Source So Far”). Also, check the progress of industry initiatives such as CDISC COSA.

Furthermore, the Using R to Submit Research to the FDA initiative completed the first part of its final Pilot 4. While the focus of the three previous pilots was on demonstrating that SDTM and ADaM packages could be created and submitted to the FDA using R — including packages that might include Shiny applications for reviewers to use — this pilot explored the use of novel technologies such as Linux containers and WebAssembly to bundle a Shiny application into a self-contained package, facilitating a smoother process of both transferring and executing the application, allowing agency reviewers to easily run and evaluate software without complex setups.

 

New releases and updates from 2024

See below a complete list of new releases and updates that occurred in 2024 with links to individual resources. Do not hesitate to spot any missing items!

 

February

• CDISC Considerations for SDTM Implementation in Observational Studies and Real-World Data v1.0

 

March

• FDA Bioresearch Monitoring Technical Conformance Guide v3.1 (Minor Language Update)

 

April

• CDISC USDM v3.0
• CDISC Analysis Results Standard v1.0 and eTFL portal
• PHUSE Data Standards White Paper
• PMDA Revision of Technical Conformance Guide on Electronic Study Data Submissions

 

June

• CDISC Tobacco Implementation Guide v1.0
• PHUSE Dataset-JSON as Alternative Transport Format for Regulatory Submissions: Final Pilot Report
• FDA Standardized Format for Electronic Submission for Marketing Applications Content for the Planning of Bioresearch Monitoring (BIMO) Inspections for Center of Biologics Evaluation and Research Submissions

 

September

• CDISC Glossary v19.0
• FDA Data Standards Catalog (Latest Release Version 10.4 / September 2024)

 

October

• PHUSE Best Practices in Data Standards Implementation Governance
• FDA Study Data Technical Conformance Guide – Technical Specifications Document (Latest Release Version 5.9 / October 2024)
• Using R to Submit Research to the FDA

 

December

• CDISC USDM v3.0 Conformance Rule Specifications
• CDISC Define-XML v2.1.8
• CDISC Dataset-JSON v1.1
• CDISC Controlled Terminology Relationships v1.0 for SDTM v1.7, SDTMIG v3.3, SDTMIG-MD v1.1
• CDISC Duchenne Muscular Dystrophy Therapeutic Area User Guide v1.0 – Cardiac Imaging Supplement
• CDISC Biomedical Concepts (Latest Release 2024-12-16)
• FDA Protocol Deviations for Clinical Investigations of Drugs, Biological Products, and Devices

 

Interested in learning more?

Download Angelo Tinazzi’s new ebook, “The Good Data Submission Doctor on Data Submission and Data Integration to the FDA”:

Written by Silvia Faini, Principal Statistical Programmer, in collaboration with Angelo Tinazzi

 

An important component of clinical trial data submission to the FDA is CDISC’s Analysis Data Model, or ADaM, which defines dataset and metadata standards. However, implementation of ADaM is not always straightforward, leading to inaccuracies and inconsistencies.

At this year’s CDISC EU+TMF Interchange in Berlin, I had the opportunity to present “visualizing ADaM: A Practical Guide Through Examples,” co-authored with Angelo Tinazzi, where I shared how visualizing ADaM provides a guided approach that can address these issues and streamline the process.

Here, we share some of the key takeaways.

 

 

Why a visual approach to ADaM?

Over the years, the CDISC and CDISC ADaM teams have released additional documents with handy ADaM use cases to the Implementation Guidance (IG), demonstrating how ADaM can be used to support the most common statistical methods or specific settings such as medical devices studies, while maintaining good traceability. Additionally, several CDISC Therapeutic Area User Guides (TAUGs) provide specific analysis examples addressing various ADaM requirements in those particular settings.

Typically, the ADaM datasets development process begins with study documents such as the Statistical Analysis Plan (SAP), accompanied by applicable CDISC ADaM guidance. By leveraging gathered knowledge and support from the company’s data governance structure, which includes tailored templates, guidance, and subject matter experts (SMEs), the statistical programmer initiates the design of ADaM datasets needed to support the analytical outputs outlined in the SAP. Despite continuous efforts to support team members with regular updates, we frequently encounter incorrect or inconsistent implementation of the standard, across multiple clients and therapeutic areas.

To enhance the implementation of ADaM by statistical programmers at Cytel, we have developed visual shells based on our standard SAP table shells. These visual shells incorporate annotations to illustrate the ADaM dataset and variables to be utilized, variables to group or categorize, as well as any filters and additional conditions. These visual shells are accompanied by sample ADaM datasets and corresponding standard specifications tailored for Cytel automation tools.

Furthermore, our development efforts extend to slide sets designed to train programmers through practical examples, ensuring a comprehensive understanding of ADaM’s application.

 

A simple example: A demographics table

The following example makes use of the standard ADSL dataset, with its standard variables, either copied from SDTM or derived in ADaM. The table shell is completed with details of variables to be selected and the rationale. For example, this demographic table filters for the safety population; as such, we expect to have in the ADSL dataset a variable containing the actual treatment received. In this case, we choose the TRT01AN variable, and the variable to filter for the applicable subjects, SAFFL. Furthermore, given the fact that our standard tools work well with numeric variables, in addition to character versions of variables such as SEX, AGEGR1, etc., we need to plan to include variables SEXN and AGEGR1N.

Figure 1: ADSL Dataset

 

Treatment-emergent adverse events table

For the analysis of treatment-emergent adverse events, we discussed two types of outputs.

In the first output, the requirement is to analyze the occurrence of the treatment-emergent adverse events and their incidence using a hierarchical medical dictionary, MedDRA, through which we summarize the occurrences by system organ class and preferred term.

 

Figure 2: ADAE Dataset

In the second output, our objective is to provide an overview of the types of adverse events that occurred. This includes determining the number of subjects who experienced at least one adverse event, the number who experienced at least one serious adverse event, identifying the most severe adverse event, and quantifying the subjects who experienced adverse events leading to either treatment or study discontinuation, among other metrics.

We could have used the same ADAE dataset created for previous output and applied selection and calculation in the analytical output program. However, to improve traceability, reproducibility (quality control), and to make the ADaM dataset analysis as ready as possible, we also have the option to create another ADAM dataset, ADAESUM, derived from ADAE, and applying a BDS structure. The annotated output shows both versions, with ADAESUM (BDS) and with ADAE (OCCDS).

Again, our example provides detailed explanations and an extract of the ADAESUM dataset.

 

Figure 3: ADAESUM Dataset

 

Change from baseline with phantom baseline visit

In this last example, I presented a table summarizing the change from baseline at each visit for vital sign parameters. For each visit, we present summary statistics for the actual observed value and the change from baseline. The peculiarity of this example is the baseline definition, defined by SAP as the average between observed value at screening and day-0 visit.

 

Figure 4: ADVS Dataset

 

From the above ADVS dataset:

• Lines 3 and 9 show the derive baseline visit with DTYPE variable, which identifies it and the method used for the derivation (AVERAGE). ABLFL is set to ‚ÄúY.‚Äù
• Lines 1, 2, 7, and 8 will not be used in the analysis (ANL01FL=Null) because they occurred before the phantom derived ‚Äúbaseline‚Äù visit, but were kept in the ADaM dataset showing traceability, e.g., from which the baseline visit was derived.
• Lines 5 and 11 will not be used in the analysis (ANL01FL=Null) because they were unscheduled visits. However, the records are kept in the ADaM dataset showing traceability so that reviewer is aware which information was not used in the analysis. Eventually, if AVISIT/AVISTN are derived using some windowing.
• All records post baseline have the change from baseline (CHG) derived, including the unscheduled visits.

 

Key takeaways

By visualizing ADaM, the choice of the ADaM structure is guided and lets the programmer select the proper dataset structure, check the need of specific variables (e.g., population flags, treatment variables), and check the presence of the data required in the analysis (e.g., collected/derived parameters, hierarchical variables).

The visual approach is highly beneficial to ADaM newcomers, streamlining ADaM specifications and programming and standard outputs production, and it gives more time to focus on non-standard outputs, which are usually more challenging than standard ones.

Internally, this is another step to improve the Cytel automation tools suite (Lighthouse, ALPS, PRISM) and to move toward a more efficient process.

“Sharing is caring” — I feel this motto well captures my feeling when presenting at conferences. It is always a great experience: sharing what we implemented or how we overcame common challenges allows a good discussion with the attendees.

 

Interested in learning more?

Download Angelo Tinazzi’s new ebook, “The Good Data Submission Doctor on Data Submission and Data Integration to the FDA”: