Evaluating Bayesian Borrowing Methods for Treatment Effect Extrapolation: A Simulation-Based Study
At ISPOR Europe 2025, Marie Génin and Antoine Movschin presented Quinten Health’s large-scale simulation study assessing Bayesian borrowing methods for extrapolating treatment effects from a source population to a target population. These methods are particularly relevant in paediatrics, rare diseases, and small-population contexts where direct evidence generation is difficult. Poster presented by Marie and Antoine […]
Comparing Individualized Treatment Effect Inference Methods Through a Simulation Study
At ISPOR Europe 2025, Antoine Movschin presented Quinten Health’s simulation-based comparison of machine-learning approaches for estimating individualized treatment effects (ITE). As healthcare increasingly relies on real-world data and personalized medicine, robust estimation of patient-level treatment effects is essential for research, clinical practice, and HTA decision-making. Antoine Movschin presenting the poster at ISPOR Europe 2025. Why […]
Comparing Individualized Treatment Effect Inference Methods Through a Simulation Study
Event: ISPOR Europe 2025, Glasgow, Scotland, UK Authors: Diane Vincent, Antoine Movschin, Tristan Fauvel View Poster OBJECTIVES Randomized controlled trials (RCTs) are the gold standard for estimating the average treatment effect (ATE), based on the equipoise principle and proper trial size and design, but they are typically not geared towards individual effect estimation. Conversely, real-world data […]
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 […]
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 […]
A text-to-tabular approach to generate synthetic patient data using LLMs
Publisher: arXivAuthors: 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 overcome […]
Artificial Intelligence (AI) ToOLs for Outbreak Detection and response: a transnational platform for surveillance, monitoring and decision support
Event: ISPOR Europe 2023, Copenhagen, Denmark Authors: M. Génin, J. Botz, L. Coudeville, M. Eisenlauer, T. Fauvel, F. Huschka, N. Lambert, D. Wang, V. Bosch Castells, C. Commaille-Chapus, B. Frandji, M. Haberstroh, JY. Robin, P. Roehn, H. Sippel, P. Thiele, E. Thommes, C. Weber, B. Amzal, H. Fröhlich, A. Kannt, C. Mahé View Poster Objectives […]
