Program of events is subject to change.
Session 1: Opening Plenary
Session 2: Global Regulatory Updates, Part I
Danish Medicines Agency Update
Sesson 3: CDISC Updates
State of the CDISC Union
State of CDISC Standards
Session 4A: ADaM
The Development of the New Analysis Results Standard
Traceability of ADaM Time to Events Data in Oncology Trials
Efficacy endpoints in oncology trials are usually divided into and response time to event parameters. Time to event parameters based on a standardized response assessment (e.g. progression free survival and event free survival based on RECIST v1.1) are closely connected to the response parameters and share many derivations. Most endpoints are based on complex derivations using data from multiple sources. For instance, the timing between assessments, which might vary during a study, and new interventions are to be considered.
When programming complex data, traceability and a structured programming are key. This paper presents some suggestions to aid the traceability, by using datapoint traceability in the analysis data for response parameters (ADRS). This will make the programming efficient, consistent, and increase the provenance of the data for both ADRS and ADTTE.
The same logic can be applied on other standardized assessments (e.g. iRECIST in treatments with immunotherapeutics), parameters, or data considerations.
ADaM Implementation Guide for Medical Devices
This paper presents ADaM Implementation Guide for Medical Devices currently under review and scheduled to be published in 2021. The ADaM IG for Medical Devices is intended to address the typical statistical analysis needs of clinical data when medical devices are used and included in analysis.
Metadata, conformance rules and examples are provided for ADaM Device-Level Analysis dataset (ADDL), Medical Device Basic Data Structure (MDBDS) and Medical Device Occurrence Data Structure (MDOCCDS).
Session 4B: Real World Data
Geographic Data and CDISC: An Example and the Case for Standardization
Standardizing location data in clinical research is ever more important given the increasingly prominent role of geography and the environment in public and clinical health settings. However, CDISC currently does not have ways to standardize various types of location data, which vary between and within countries. However, doing so would allow for sensitive geographic information to be handled in an appropriate manner and also be applied across clinical studies to augment research.
We present a case study of using GPS coordinates from a CDISC-standardized malaria clinical study to examine study participant movement. Retroactive data analysis of location and time data from study participants allowed for computation of trajectories which in turn allowed for analysis of movement during mosquito biting hours over the course of the study. This study shows potential of using geographic data within CDISC standards, which could reduce variation in methods in other studies attempting similar research.
Data Standards and COVID-19: CDISC Involvement in the IMI DRAGON Project
The IMI-funded project “RapiD and SecuRe AI enhAnced DiaGnosis, Precision Medicine and Patient EmpOwerment Centered Decision Support System for Coronavirus PaNdemics”, or DRAGON, was launched in October 2020. The project aims to apply artificial intelligence and machine learning to deliver a decision support system for improved and more rapid diagnosis and prognosis of COVID-19. The DRAGON partners envision the implementation of a federated data system that will enable multiple stakeholders from around the globe to contribute both legacy and prospectively-collected data. The disparate nature of such diverse data and data collection methods produced by contributors presents unique challenges to data standardisation. This presentation will discuss CDISC’s involvement in the DRAGON initiative.
Expanding the OMOP Observational Data Standard to Capture Clinical Trial Data
The OMOP data standard was created for observational health data.
The data types that are generally collected during clinical trials have a large degree of overlap with those of observational data, e.g., the same kind of lab measurements. However, a significant gap exists in representing many of the distinctive features of clinical trial data.
OMOP is maintained by the OHDSI community of industry and academia collaborators. Several of its working groups focus on expanding OMOP’s applicability to other research areas.
The OHDSI Clinical Trials working group proposes conventions for the OMOP standard to capture clinical trial specific data.
- Our use case is to convert clinical trial data in CDISC SDTM format to OMOP, with a view to allowing trial planning optimization.
- We advocate minimum changes to the OMOP common data model and terminology to minimize impact on OHDSI analytics tools, whilst providing a value-add SDTM-to-OMOP conversion with minimum data loss.
This presentation will introduce the eight main topics for which there is currently insufficient support in OMOP (from trial information and visits, to adverse events and their seriousness, severity and causality), and describe the next steps to bridge the gap between OMOP and SDTM for these important clinical concepts.
Session 4C: CDISC Foundational, Part I
Introduction to the CDASH eCRF Project
The CDASH team establishes the metadata standards for clinical data collection. Companies across the industry use the CDASH standards when developing electronic CRFs in EDC systems, which can be a complex and costly process. To simplify this process, CDISC started the eCRF Portal Project, with the aim of creating CDASH-compliant eCRFs that can be uploaded as electronic definition files in various EDC systems. The goal is to reduce the cost of eCRF development for industry and increase compliance to the standard.
The CDASH eCRF Portal Team is currently working on this project using the Data Acquisition Designer provided by Formedix. The eCRFs are freely accessible on the CDISC website. The purpose of this presentation is to provide an update on the progress to date and layout steps for further development.
Safety User Guide
Over the years, CDISC and the Community have created foundational models that provide a robust and commonly accepted standard to collect, tabulate and analyze research data. These foundational standards have become the de facto standard to exchange data between sponsors, contract research organizations and regulatory agencies.
CDISC has initiated the development of a Safety User Guide. This Safety User Guide will focus on standardizing the most commonly performed safety analyses across studies. By standardizing statistical outputs such as listings, figures and tables it is much easier to standardize the underlying statistical analysis and tabulations datasets needed to generate these outputs. This user guide will show CDASH, SDTM, and ADaM examples.
Once the industry starts adopting the most common safety assessments, the variability in using the standards for these common data will reduce dramatically.
SEND Development: Improving the Next Generation of Nonclinical Submission Datasets
Session 5A: COVID-19 Topics
Handling COVID-19 Impact in an Ongoing Trial
There is no doubt that the COVID 19 global pandemic had a major impact on the ongoing trials. Both CDISC and the FDA issued guidance on how to handle it and what is expected to be reported.
The Pandemic impacted various aspect of the clinical trials e.g. recruitment of subjects, missed visits, missed assessments in addition to new safety issues…
The presentation will show you how the teams managed the COVID19 impact information in the eDC, using minimal update of the design of eDC.
We will also discuss the impact on SDTM and ADaM datasets: the addition of new domains/ADs, and new NSV to the existing domains/ADs. In addition to the impact on the submission documentation
Track COVID-19 Deviations while Remaining CDISC Compliant
The global pandemic has been disruptive to clinical study operations, impacting the planned data collection and its processing. It is vital to both the sponsor and the regulatory organisations that we are able to trace subject information that have been significantly influenced by the circumstances perpetuated due to the pandemic, everything from site closure, to simultaneous study participations, partial or delayed assessments. At Novo Nordisk, we have implemented a CDISC compliant solution to ensure clear identification of such information and thus enable traceability and insights on our study data affected by the pandemic. Our solution builds on current CDSIC standards and is believed to be robust enough to be replicated and scaled to cover other unplanned global events that may adversely affect our clinical studies and would like to present this to the broader pharma community.
Challenges in Fast-tracked Covid-19 Submissions
This presentation highlights CDISC/FDA COVID-19 guidelines and challenges faced in interpreting COVID-19 guidelines for fast-tracked Covid-19 FDA submissions.
The year 2020 saw the worst COVID-19 pandemic that hit the whole world. Every industry was impacted including pharmaceutical companies, Clinical Research Organizations (CROs) and Biotech firms. Without clear regulatory guidelines in place until March 2020, many pharmaceutical companies, CROs and Biotech firms were all left scrambling for clear guidance.
This paper attempts to look at challenges faced with fast-tracked COVID-19 studies and how CROs came up with innovative methods such as remote monitoring to deliver on fast-tracked studies.
Analyzing Data Missing Due to COVID-19: from Raw Data to ADaM and Beyond
The COVID-19 pandemic has a tremendous impact on everyone - and on the pharmaceutical industry. Naturally, those who are developing drugs took the first hit trying to treat it; however, statistical programmers who are working on any ongoing study had to reconsider their activities and timelines as well. New analyses need to be performed and the existing ones need to be amended taking lots of missing and incomplete data into account. CDISC has published the “Interim User Guide for COVID-19”, which describes how to represent COVID-19 related data with CDASH and SDTM. In my presentation, I would like to go one step further and walk you through an example of preparing this data for analysis from mapping it to the VE SDTM domain introduced by CDISC, to creation of the corresponding ADaM dataset, and then to the R shiny app supporting the analyses team was required to perform.
Session 5B: Tech-Enabled Standards
Implementation of an MDR
Clinical standard initiatives and regulations have driven the need for defining standards across all functions of clinical trial activities. Different companies maintain industry and/or company standards in different ways. However, to create a holistic solution which on one hand allows governance teams to effectively track standards and on the other hand also aids study teams during setup, conduct and analysis one must consider the creation of metadata repository along with all necessary functionalities.
A well architected and implemented MDR system can bring immense value to business. It will help to reduce cost & clinical development timelines and at the same time increases the quality of data by defining golden copy of standards, promoting reusability, automating processes & bringing end-to-end traceability. In this presentation, we will be going through some of the added values of having a metadata repository as well as the challenges that can be faced while implementing an MDR.
Accelerating SDTM Generation with AI/ML
The CDISC Study Data Tabulation Model (SDTM) is a mandatory standard for regulatory submissions of clinical trial data. As the study designs are getting complex day by day, it adds complexity and variance to the CRFs and third party data. Henceforth, generation of SDTM datasets takes more time and is resource expensive. This presentation explains how we can improve the time to submission by leveraging Artificial Intelligence and Machine Learning in SDTM generation process. The deep learning techniques extracts knowledge from global standards, past submissions to produce the outputs like SDTM specifications, programs etc. The solution approach includes a systematic process where the generated outputs help in easy review and update of SDTM datasets. In the presentation, we will also explain how the updates done by the Clinical Data Managers and Clinical Programmers would help in training the model.
Yes, You Can Access the CDISC Library from SAS
With the availability of the CIDSC Library, vendors can now develop software from which you can instantly access standards e.g. Controlled Terminology. This is also true for users working with traditional software such as SAS.
In SAS, the CDISC Library can be queried through the CDISC API using PROC HTTP; its code is pretty simple and you can easily “interpret answers” e.g. json or xml.
The main purpose of this presentation is to provide my first “impressions” on the use of CDISC Library especially from the SAS user perspective. I will first discuss requirements to access the CDISC Library and the two different methods to access it, “Data Standards Browser” vs “API queries”. I will then focus on the API use from SAS and the main “request” you can make. The Current limitations of the CDISC library will be also discussed.
Automated Generation of CDISC Biomedical Concepts Starting from Healthcare Terminologies
CDISC Biomedical Concepts (BCs) are being developed as basic protocol building blocks allowing automated generation of CRFs, data collection, and up to SDTM and ADaM table generation.
Such (machine-readable) concepts are however not only used in clinical research. Similar concepts are already used for a longer time in healthcare.
In our presentation, we will show how "LOINC Panels" which are pre-defined sets of tests routinely being used in healthcare, can be transformed in a semi-automated way into CDISC BCs (and stored as Linked Data graphs) using the by CDISC published LOINC-CDISC mapping, and our extensions for vital signs, Corona-virus tests, and ECGs.
For example, the LOINC panel 85354-9 "Blood Pressure Panel" corresponds to the CDISC-BC "blood pressure" and can easily be derived from it. However, hundreds of other BCs can be generated in a similar semi-automated way, not only for vital signs, but also for ECG data and for Coronavirus microbiology tests.
As, during the generation of the BCs, we also include mappings of the possible test results ("LOINC Answers") to SNOMED-CT using UMLS (the Unified Medical Language System), the generated graphs are extended with SNOMED-CT terms and codes. This allows the "extended BC" to be used in the retrieval and mapping of data from electronic health records (which use LOINC and SNOMED-CT all the way).
During the presentation, we will demonstrate the software that we developed, and generate a number of BCs "on-the-fly".
Session 6A: CDISC Foundational, Part II
Articulating ADaM Design Decisions: How to Communicate Your Analysis Dataset Designs
The CDISC standards have brought transparency to data handling processes and statistical analyses in clinical trials. Ever since the Japanese regulatory authority, PMDA had started accepting e-submission in 2016, many SDTM and ADaM datasets have been created in pharmaceutical companies and contract research organizations. At the same time, difficulties have arisen when reviewing ADaM datasets someone else created. This is mostly due to difficulty in accurately communicating ADaM design to others. In this paper, some reason why it is difficult to communicate ADaM design to others, design decisions when making ADaM, standards and templates, how to avoid and resolve these problems to successfully reach the goal in projects, are discussed.
Reporting Protocol Deviation in SDTM
As laid out in the SDTM Implementation Guide, sponsors are to “capture protocol violations and deviations during the course of the study” in DV, an Event domain. However, unlike other clinical data, protocol deviations are not collected on CRF but are typically reported using sponsor’s clinical trial management system. This poses challenges in the standardising of terminologies across trials and their subsequent use in ADaM and reporting.
In this presentation, I will share Novo Nordisk’s approach in reporting subject and non-subject level protocol deviations in SDTM as well as on how to support the evolving business needs and requirements for submission.
The Future of Lab Data Mapping
The development of new, and expansion of existing, lab-like domains across different versions of SDTMIG has necessitated the movement of certain types of lab data from the LB domain into new or existing domains. The addition of new variables across different version of the SDTM will allow sponsors to transition away from the use of some supplemental qualifiers. Starting March 2021, the FDA will require new clinical data submissions to be performed according to SDTM version 1.7 and SDTM Implementation Guide (IG) version 3.3. Additionally, the SDS team is planning to publish SDTMv2.0 and SDTMIGv3.4 in 2021..
With this in mind, we will provide an overview of the changes in the LB domain across SDTMIGv3.3/SDTMv1.7 and SDTMIGv3.4/SDTMv2.0. We will discuss what variables are being added or changed in the LB domain, how the mapping of certain lab data types will change, and how to implement future updates in existing SDTM/SDTMIG versions.
Session 6B: Global Submissions
The Challenges of Submitting to FDA and PMDA simultaneously
China NMPA Data Submission: Regulatory Requirements and Validation
(1) Overview of CDISC in China; (2) History of regulatory requirements of data submission in China (3) Requirements detailed in NMPA Guidance on Clinical Trial Data Submission and How to check the conformance.
Incorporating the FDA BIMO and TransCelerate Guidance on Protocol Deviations into the DV Domain
Session 7: CDISC 360 Update
CDISC 360 Implementation Plan 2021
Behind the Scenes with CDISC Data Science
CDISC Data Science seeks to deliver the CDISC standards such that they maximize an implementer's ability to automate processes. Some of the Data Science work is well-known to the CDISC community, such as the Library and Data Exchange Standards like Define-XML. Much of the behind the scenes work is less visible, yet will help drive the future of CDISC standards implementations. This presentation highlights many of the current Data Science projects as well as future initiatives to expand the CDISC Library, generate new standards content, provide new data exchange capabilities, implement CDISC 360, provide HL7 FHIR mapping, support open source software development, release new web site capabilities, and enhance the way we work with the existing standards.
Biomedical Concepts Discussion
This presentation will report on that collaborative curation project and demonstrate the benefits Biomedical Concepts (BCs) can bring in the end-2-end data process, both today and in the future.
The presentation will provide an overview of the project and the steps taken to develop a draft BC library. This will be followed by a section on using BCs today and how using BCs will aid the community in the future and what is possible, including linking CRFs and SDTM, the use in End Points and Objectives as part of Study Design and Protocols BC and their use in varied Data Sources.
The presentation will then turn to change management issues related to BCs and how they may impact individuals in their day-to-day work.
The presentation will close with a summary including lessons learned, where the various resources can be found and the next steps for the project.
Session 8: Closing Plenary - Regulatory Presentations, Part II
New Drug Therapy Approval Success During Challenging Times
Facilitating the Development of COVID-19 Vaccines
Exhibitor and Attendee Virtual Meetups
This is time in addition to our regular conference hours for dedicated, 1:1 meetings between attendees and our valued exhibitors.
2021 CDISC TechniCon
Mapping EHR Data to EDC - A Case Study
System Automation for CDISC Standards: SDTM-ETL
System Automation for CDISC Standards: A3 MDR
How to Comply with CDISC Standards in Clinical Trial Design and Build
CDISC Library Browser
System Automation for CDISC Standards: elluminate®
National Cancer Institute Enterprise Vocabulary Services (EVS) REST API