6 papers
leaspy: LEArning Spatiotemporal Patterns in PYthon
Juliette Ortholand, Sofia Kaisaridi, Nicolas Gensollen +14
Longitudinal data are fundamental across scientific disciplines for modeling how complex systems evolve over time. A core challenge in these settings is handling temporal misalignm…
Longitudinal Outcomes Truncated by Death: Causal Estimands and Bayesian Estimators
Juliette Ortholand, Young Lee, Marie-Abele C Bind
In randomized controlled trials with longitudinal outcomes, death before the end of follow-up poses a fundamental challenge: after death, the outcome is no longer a real-valued mea…
A Causal Framework for Evaluating ICU Discharge Strategies
Sagar Nagaraj Simha, Juliette Ortholand, Dave Dongelmans +4
In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with…
A mixture model for subtype identification in the context of disease progression modeling
Sofia Kaisaridi, Juliette Ortholand, Caglayan Tuna +2
The progression of chronic diseases often follows highly variable trajectories, and the underlying factors remain poorly understood. Standard mixed-effects models typically represe…
A joint spatiotemporal model for multiple longitudinal markers and competing events
Juliette Ortholand, Stanley Durrleman, Sophie Tezenas du Montcel
Non-terminal events can represent a meaningful change in a patient's life. Thus, better understanding and predicting their occurrence can bring valuable information to individuals.…
Joint model with latent disease age: overcoming the need for reference time
Juliette Ortholand, Nicolas Gensollen, Stanley Durrleman +1
Introduction: Heterogeneity of the progression of neurodegenerative diseases is one of the main challenges faced in developing effective therapies. With the increasing number of la…