CDISC Interchange 2024
Implementing CDISC CORE in a cloud native, regulated environment supporting Rare Diseases submissions
by Emmy Pahmer (with Sandeep Juneja, argenx)
CDISC (executable) Conformance Rules are an integral part of the Foundational Standards and serve as the specific guidance to Industry for the correct implementation of the Standards in clinical studies. CDISC provides free access to CORE to CDISC members and non-members.
A regulated environment refers to a controlled setting where specific rules, regulations, and standards are in place to ensure the quality, safety, and efficacy of products and processes related to healthcare and life sciences. Companies operating in the life sciences sector must comply with these regulations to ensure that their products are safe, effective, and of high quality. Several Commercial Off-The-Shelf (COTS) applications are available for this purpose.
One such application is the SAS Life Science Analytics Framework (LSAF), providing a 21 CFR Part 11 compliant web-based Statistical Computing Environment (SCE) and Structured Data Repository where study data can be securely stored with versioning and audit history capabilities.
The focus of our presentation is to share with the CDISC community our journey implementing the CDISC CORE engine and rules within the SAS LSAF environment at argenx, a global immunology company, committed to improving the lives of people suffering from severe autoimmune diseases.
We will highlight challenges encountered and insights gained during the implementation process. We also plan to perform a gap analysis running the CORE rules for rare diseases and share potential plans for supporting the development of additional CORE rules closing these identified gaps in the near future.
The presentation aims to be a comprehensive guide for those seeking to implement CDISC CORE. By sharing practical insights, challenges, and lessons learned, it contributes to the ongoing discourse on best practices in clinical studies data management and regulatory compliance. As argonauts we strongly believe in collaboration and knowledge sharing driving further adoption of CORE within our industry.
Setup of ADAE and ADTTE for
Exposure-Adjusted Incidence Rate Reporting in an Integrated Summary of Safety (ISS) submission
by Mitchikou Tseng
Reporting Exposure-Adjusted Incidence Rate (EAIR) can be part of an Integrated Summary of Safety (ISS) submission since the regulatory agency can be interested on the drug exposure duration of the subjects until a certain adverse event (AE) occurred. The programming however, can be a challenging task, especially since the subject population in an ISS is huge and various AE categories must be reported. The adverse events analysis dataset (ADAE) can be used directly to report results in your table utilizing a macro, or alternatively a time to event analysis dataset (ADTTE) can be additionally created to facilitate easy reporting of the EAIR. This paper will talk about how to setup ADAE and ADTTE to support the EAIR analysis and why this approach is more efficient as compared to deriving EAIR at the level of table generation. Furthermore, some challenges related to integrated database development and reporting, such as huge dataset processing impacting program run times, inconsistent availability of information between individual studies resulting to more complex variable derivations and the laborious validation of analysis results against previously available reports of the individual studies, will be discussed alongside with tips and best practices to overcome these challenges effectively.
A custom domain for induced pain, going from source to SDTM to ADaM
by Caro Sluijter (with Jelle van Hasselt, Centre of Human Drug Research)
Early-phase clinical trials regarding the analgesic effects of a novel study drug necessitate the induction of pain in healthy volunteers to evaluate the drug. The Centre for Human Drug Research (CHDR) has developed PainCart, a comprehensive battery of tests to assess the efficacy of analgesic compounds by administering a wide variety of pain stimuli including thermal, electrical, chemical and mechanical pain.
As this test battery is used in clinical trials that may later be submitted to the FDA, the results of PainCart tests need to be converted into SDTM and ADaM datasets. However, due to the distinct nature of the pain stimuli, there are no known SDTM domains to store this information. Furthermore, the existing therapeutic area user guides (TAUG) regarding pain are related to patients suffering from chronic pain, which is not relevant for these studies, where pain is induced in healthy volunteers.
To overcome this challenge, we created a custom SDTM domain, called XP. This presentation highlights the process and challenges involved in creating a custom PainCart SDTM domain, and shows the conversion from SDTM XP to ADaM XP.