Background Few studies on the risk of incident major adverse cardiac and cerebrovascular events (MACCEs) in sarcopenia have been reported. The objective was to assess the association between presarcopenia and sarcopenia and a higher risk of MACCEs. Methods This study on the UK Biobank prospective cohort, used data collected between 2006 and 2021....

Heart failure (HF) remains a main cause of mortality worldwide. Risk stratification of patients with systolic chronic HF is critical to identify those who may benefit from advanced HF therapies. The aim of this study is to identify plasmatic proteins that could predict the early death (within 3 years) of HF patients with reduced ejection fraction hospitalized in CHRU de Lille....

Clustering conceptually reveals all its interest when the dataset size considerably increases since there is the opportunity to discover tiny but possibly high value clusters which were out of reach with more modest sample sizes. However, clustering is practically faced to computer limits with such high data volume, since possibly requiring extremely high memory and computation resources....

Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions related by some neighborhood structure (spatial, group, etc.)....

Abstract Importance Metabolic flexibility, a key hallmark of cancer, reflects aberrant tumour changes associated with metabolites. The metabolic plasticity of pancreatic neuroendocrine tumours (pNETs) remains largely unexplored. Notably, the heterogeneity of pNETs complicates their diagnosis, prognosis, and therapeutic management....

Background: In many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution. In such frameworks joint modeling is of high interest....

The MLGL R-package, standing for Multi-Layer Group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data....

This paper proposes the first model-based clustering algorithm for multivariate functional data. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, {based on the assumption of normality of the principal components}, is defined and estimated by an EM-like algorithm....

Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance....

Statisticians are already aware that any modelling process issue (exploration, prediction) is wholly data unit dependent, to the extend that it should be impossible to provide a statistical outcome without specifying the couple (unit,model)....