16 citations · 49 across the 38 of their papers we have counts for
11 papers · 1 filter
Generalized propensity score weighting for functional causal inference framework
Simone Ciardulli, Nicole Fontana, Simone Vantini +1
Estimating causal effects in observational studies requires adjustment for confounding, a task that becomes challenging when the exposure is a function observed over a continuous d…
Trajectory-informed graph-based clustering for longitudinal cancer subtyping
Lara Cavinato, Marco Rocchi, Luca Viganò +1
Cancer subtyping plays a crucial role in informing prognosis and guiding personalized treatment strategies. However, conventional subtyping approaches often rely on static, biopsy-…
A neighbour selection approach for identifying differential networks in conditional functional graphical models
Alessia Mapelli, Laura Carini, Francesca Ieva +1
Estimating how different brain regions communicate with each other using EEG data is valuable both for medical research and clinical diagnosis. This involves quantifying the statis…
Modeling group heterogeneity in spatio-temporal data via physics-informed semiparametric regression
Marco F. De Sanctis, Eleonora Arnone, Francesca Ieva +1
In this work we propose a novel approach for modeling spatio-temporal data characterized by group structures. In particular, we extend classical mixed effect regression models by i…
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients
Chiara Masci, Marta Spreafico, Francesca Ieva
In the context of clinical and biomedical studies, joint frailty models have been developed to study the joint temporal evolution of recurrent and terminal events, capturing both t…
Dynamic treatment effect phenotyping through functional survival analysis
Caterina Gregorio, Giovanni Baj, Giulia Barbati +1
In recent years, research interest in personalised treatments has been growing. However, treatment effect heterogeneity and possibly time-varying treatment effects are still often…
Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach
Alessandra Ragni, Chiara Masci, Francesca Ieva +1
We introduce a novel statistical significance-based approach for clustering hierarchical data using semi-parametric linear mixed-effects models designed for responses with laws in…
Mining and evaluation of patients' diagnostic therapeutic paths through state sequences analysis
Laura Savaré, Francesca Ieva, Giovanni Corrao +1
The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. Nevertheless, recommendations regarding…
Imaging-based representation and stratification of intra-tumor Heterogeneity via tree-edit distance
Lara Cavinato, Matteo Pegoraro, Alessandra Ragni +3
Personalized medicine is the future of medical practice. In oncology, tumor heterogeneity assessment represents a pivotal step for effective treatment planning and prognosis predic…
How much of the past matters? Using dynamic survival models for the monitoring of potassium in heart failure patients using electronic health records
Caterina Gregorio, Giulia Barbati, Arjuna Scagnetto +2
Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term care of subjects affected by chronic illness…
A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data
Andrea Martino, Andrea Ghiglietti, Francesca Ieva +1
This paper proposes a clustering procedure for samples of multivariate functions in , with . This method is based on a k-means algorithm in which the distance…