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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. |
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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. |
