Contribution à la sélection de variables en présence de données longitudinales : application à des biomarqueurs issus d'imagerie médicale

Abstract : Clinical studies enable us to measure many longitudinales variables. When our goal is to find a link between a response and some covariates, one can use regularisation methods, such as LASSO which have been extended to Generalized Estimating Equations (GEE). They allow us to select a subgroup of variables of interest taking into account intra-patient correlations. Databases often have unfilled data and measurement problems resulting in inevitable missing data. The objective of this thesis is to integrate missing data for variable selection in the presence of longitudinal data. We use mutiple imputation and introduce a new imputation function for the specific case of variables under detection limit. We provide a new variable selection method for correlated data that integrate missing data : the Multiple Imputation Penalized Generalized Estimating Equations (MI-PGEE). Our operator applies the group-LASSO penalty on the group of estimated regression coefficients of the same variable across multiply-imputed datasets. Our method provides a consistent selection across multiply-imputed datasets, where the optimal shrinkage parameter is chosen by minimizing a BIC-like criteria. We then present an application on knee osteoarthritis aiming to select the subset of biomarkers that best explain the differences in joint space width over time.
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Julia Geronimi. Contribution à la sélection de variables en présence de données longitudinales : application à des biomarqueurs issus d'imagerie médicale. Algorithme et structure de données [cs.DS]. Conservatoire national des arts et metiers - CNAM, 2016. Français. ⟨NNT : 2016CNAM1114⟩. ⟨tel-01668517⟩

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