Working Group Events Report – Q4 2024
- Nicola Newton
- Lucy Marks
- Alex Pearce
| Working Group Events |
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Working Groups have held a total of 18 virtual events this year! From Community Forums to webinars, the bi-annual Data Transparency Events and a new Open Source Series.The attendance for Working Group events in 2024 has been evaluated; in total, we had 2,343 attendees across the various Working Group virtual events in 2024, a fantastic achievement by all of the project teams and project leads to generate such engagement. Data Transparency Virtual events had a huge 865 attendees across the two events held in 2024 and Working Group Webinars, Community Forums and Open Source events had 1478 attendees in 2024! |
| Open Forums |
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The Open Source Technology in Clinical Data Analysis (OSTCDA) project was set up with the aim to create a manuscript on the integration of open-source software solutions for clinical data management, analysis and reporting. A significant amount of time and energy has been invested in recent years exploring the desirability (do we want it?), feasibility (can we do it?), and viability (is it worth it?) of integrating open source solutions into our clinical data pipelines which transform source data into clinical study reports and submission data packages. In this October edition of the Open Source Open Forums, we will update on the status of this initiative and continue to hear from you on what we’ve missed so far. |
| Community Forums |
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RWD Sources – How the Genesis of Your Data Determines What Questions You Can Answer When considering sources of RWD, it is important to consider more than just the number of available patients. Recently released guidance from the FDA encourages researchers to build accurate, complete and traceable real-world datasets. Combining data from structured and unstructured electronic health record data, closed claims, and other sources is essential to building the patient journey. Responsive Regulation of AI in Drug Development The use of artificial intelligence (AI), including machine learning (ML), technologies across all stages of the drug product life cycle may accelerate the delivery of safe and effective high-quality drugs. As this data-driven technology continues to rapidly evolve across the landscape of drug development, a responsive regulatory approach may be warranted to calibrate the requirements needed to meet safety and evidentiary standards. This responsive regulatory approach can be based on an assessment of model risk, which is estimated by examining AI models’ influence on regulatory decision-making and the potential consequences of wrong decisions if the model is inaccurate. This responsive regulatory approach is rooted in an in-depth understanding of the specific application context and calibrates regulatory requirements in accordance with model risk. |
| Safety Analytics Webinar |
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Recommendations for Adverse Event Collection and Treatment Emergent Definition In 2019, the PHUSE Best Practices for Data Collection Initiatives project team, in conjunction with the Analysis and Display of Safety Analytics project team, conducted a survey (link) to study the variation in the collection and definition of treatment emergent adverse events (TEAEs) in clinical studies. It noted the need to pursue additional research to further harmonise industry practices. The PHUSE Adverse Event Collection Recommendations and the Treatment Emergent Definitions Recommendations project teams were formed to develop recommendations to reduce the implementation variability. |
