PHUSE EU Connect 2024

Our colleagues have submitted a total of four abstracts for Phuse EU Connect 2024 in Strasbourg.
You can read them below. We are curious to hear your thoughts. Read them here.

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. Furthermore, some challenges related to integrated database development and reporting 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 Riya Rai (with Jelle van Hasselt, CHDR)

Early-phase clinical trials regarding analgesic effects of a novel study drug necessitate induction of pain in healthy volunteers. The Centre for Human Drug Research (CHDR) has developed PainCart, a comprehensive battery of tests to assess efficacy of analgesic compounds by administering a wide variety of pain stimuli.As this test battery is used in clinical trials that may later be submitted to the FDA, results of PainCart tests need to be converted into SDTM and ADaM datasets. However, there are no known SDTM domains to store this pain stimuli information. Furthermore, therapeutic area user guides (TAUG) regarding pain are related to chronic pain, which is not relevant for 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 the conversion to ADaM.

Implementing CDISC CORE in a cloud native, regulated environment supporting Rare Diseases submissions

by Emmy Pahmer (with Sandeep Juneja, argenx)   

The focus of our presentation is to share with the CDISC community our journey implementing the CDISC Open Rules Engine (CORE) within the SAS Life Science Analytics Framework (LSAF) environment at argenx. CORE is a free open-source tool for validating CDISC and regulatory conformance. LSAF is a closed system which provides 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. Argenx is 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 an evaluation of CORE functionality, running the rules for rare disease studies. We will also share potential plans for the future in supporting the development of the CORE engine.

From source to submission: Getting the best of multiple standards

by Berber Snoeijer, ClinLine (with Jules van der Zalm)

Enabling an end-to-end data flow of real-world data from source to regulator involves a number of different data standards. As FHIR’s main use is the exchange of electronic health record information through JSON formatted files, it falls short in data selection and fit-for-purpose evaluation. On the other hand, direct transfer to CDISC SDTM would entail extensive mapping, which might not be necessary for the majority of patients. Moreover, SDTM format is not optimal for fit-for-purpose assessments and imputation. We propose aligning and standardizing the essential intermediary stage of data transformation. The OMOP standard serves this purpose very well as it includes a number of source traceability variables and standardized mapping of concepts. In this presentation, we will demonstrate how we add custom variables to the OMOP standard to allow for full end-to-end traceability and seamless mapping to SDTM.