Navigating the Real World: A Scoping Review of Structured Frameworks to Effectively Identify, Evaluate, and Select Real-World Data Sources for Fit-for-Purpose Studies
Event: EuroDURG 2025, Uppsala, Sweden Authors: Sonia Zebachi, Julien Tanniou, Elisabeth Bakker, Sieta T. de Vries, Rossella di Bidino, Entela Xoxi, Anna Glaser, Gianluigi Savarese, Jan Hillert, Peter G.M. Mol, Kelly Plueschke, Ghinwa Hayek, Billy Amzal, Jeverson Moreira View Poster The potential of real-world data (RWD), particularly from patient registries, has been increasingly recognised over […]
Policy makers must adopt agile signal detection tools to strengthen epidemiological surveillance and improve pandemic preparedness
Publisher: Health Policy Authors: Cédric Mahé, Aimo Kannt, AIOLOS Consortium View publication Abstract The SARS-COV2 pandemic has highlighted the urgent need for agile and responsive disease surveillance systems. To strengthen epidemiological surveillance and improve pandemic preparedness, policymakers must adopt real-time signal detection tools that integrate multisource data, including non-traditional health data, advanced analytics, and artificial […]
A text-to-tabular approach to generate synthetic patient data using LLMs
Publisher: IEEE XploreAuthors: Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert, Mathilde Berthelot, Antoine Movschin View publication Abstract Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and high costs. To […]
A text-to-tabular approach to generate synthetic patient data using LLMs
Event: IEEE ICHI 2025, Rende, ItalyAuthors:Margaux Tornqvist, Jean-Daniel Zucker, Tristan Fauvel, Nicolas Lambert, Mathilde Berthelot, Antoine Movschin View Conference Proceeding Abstract Access to large-scale high-quality healthcare databases is key to accelerate medical research and make insightful discoveries about diseases. However, access to such data is often limited by patient privacy concerns, data sharing restrictions and […]
Decision-Making Criteria and Methods for Initiating Late-Stage Clinical Trials in Drug Development from a Multi-Stakeholder Perspective: A Scoping Review
Event: PSI 2025, Verona, Italy Authors: Ce Jiang, Céline Beji, Sonia Zebachi, Ghinwa Y. Hayek, Aysun Cetinyurek-Yavuz, Muhammad Bergas N. Fayyad, Laura Rodwell, Kit C. B. Roes, Billy Amzal, Christoph Gerlinger, Raphaël Porcher, Julien Tanniou View Abstract The decision-making process in drug development involves “go/no-go” decisions, particularly at the transition from early to late-stages trials. While the decisions are solely made by drug developers, they must […]
Federated Learning: A Privacy-Preserving Approach to Data-Centric Regulatory Cooperation
Publisher: Frontiers in Drug Safety and RegulationAuthors: Alexander Horst, Paul Loustalot, Sanjeev Yoganathan, Ting Li, Joshua Xu, Weida Tong, David Schneider, Nicolas Löffler-Perez, Erminio Di Renzo, Michael Renaudin View Publication Abstract Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the […]
Analyzing recurrent events in multiple sclerosis: a review of statistical models with application to the MSOAC database
Publisher: Journal of NeurologyAuthors: David Herman, Julien Tanniou, Emmanuelle Leray, Chloe Pierret, Quentin Pilard View Publication Abstract Patients with multiple sclerosis (MS) are susceptible to experience recurrent events of disability progression and relapses. Many studies still focus on analyzing MS events with traditional methods such as Cox proportional hazards, Poisson, and logistic regression that either […]
Comparison of Bayesian methods for extrapolation of treatment effects: a large scale simulation study
Publisher: arXivAuthors: Tristan Fauvel, Julien Tanniou, Pascal Godbillot, Billy Amzal View Publication Abstract Extrapolating treatment effects from related studies is a promising strategy for designing and analyzing clinical trials in situations where achieving an adequate sample size is challenging. Bayesian methods are well-suited for this purpose, as they enable the synthesis of prior information through the use of […]
AI-Driven Disease and Care Modeling and Simulation: A Framework for Simulating Real-World Scenarios in Non-Small Cell Lung Cancer (NSCLC)
Event: ELCC 2025, Paris, France Authors: Antoine Movschin, Lise Bosquet, David Pérol, Melissa Rollot, Louise Dry, Coriande Clemente, Farah Al Nakib, Mathilde Berthelot, Margaux Törnqvist View Poster Background Advances in personalized medicine have driven targeted therapy for NSCLC through mutation identification, but their rarity in real-world (RW) settings limits traditional RCTs. External control arms (ECAs) offer an […]
Decision-Making Criteria and Methods for Initiating Late-Stage Clinical Trials in Drug Development from a Multi-Stakeholder Perspective: A Scoping Review
Publisher: Clinical Pharmacology and Therapeutics Authors: Ce Jiang, Céline Beji, Sonia Zebachi, Ghinwa Y. Hayek, Aysun Cetinyurek-Yavuz, Muhammad Bergas N. Fayyad, Laura Rodwell, Kit C. B. Roes, Billy Amzal, Christoph Gerlinger, Raphaël Porcher, Julien Tanniou View Publication The decision-making process in drug development involves “go/no-go” decisions, particularly at the transition from early to late-stages trials. […]
