5 papers
Calibrating confounding strength in sensitivity models for weighting estimators: a comparative review and a new method
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2
Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesi…
Latent Neural-ODE for Model-Informed Precision Dosing: Overcoming Structural Assumptions in Pharmacokinetics
Benjamin Maurel, Agathe Guilloux, Sarah Zohar +2
Accurate estimation of tacrolimus exposure, quantified by the area under the concentration-time curve (AUC), is essential for precision dosing after renal transplantation. Current…
Toward Valid Generative Clinical Trial Data with Survival Endpoints
Perrine Chassat, Van Tuan Nguyen, Lucas Ducrot +2
Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases…
A regularized multi-state model for covariate selection with interval-censored survival data
Ariane Bercu, Agathe Guilloux, Cécile Proust-Lima +1
In population-based cohorts, disease diagnoses are typically censored by intervals as made during scheduled follow-up visits. The exact disease onset time is thus unknown, and in t…
Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2
In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxin…