Author

A. Guilloux

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Preprint Sep 2026

Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes

Time-to-event outcomes are central in oncology and rare diseases, where treatment effects are often summarized by differences in survival curves or Restricted Mean Survival Time (RMST). In real-world data, estimating these causal effects relies on the absence of unobserved confounding, an assumption that is rarely sati...

Jean-Baptiste Baitairian, Bernard Sebastien, R. Jreich et al. · 0 citations

#machine learning Preprint Oct 2026

HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation

Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning. Realistic synthesis of patient-level clinical data requires jointly modeling heterogeneous static covariates, irregularly sampled longitudinal trajectories, and inform...

P. Chassat, A. Guilloux · 0 citations