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Modélisation et prédiction conjointe de différents risques de progression de cancer à partir des mesures répétées de biomarqueurs

Abstract : In longitudinal studies in cancer, a major problem is the description of the patient’s disease evolution or the prediction of his future state, based on repeated measurements of a biological marker. Joint modelling enables to meet these objectives but it has mainlybeen developed for the simultaneous study of a Gaussian longitudinal marker and a single event time. In order to characterize the transitions between successive events that a patient may experience, we extend the classical methodology by introducing a joint model for a Gaussian longitudinal process and a non-homogeneous Markovian multi-state process. The model assumes that individual transition times are independent conditionally to included covariates. We also propose a score test to assess this assumption. These developments are applied on two cohorts of men with localized prostate cancer treated with radiotherapy. The model quantifies the impact of prostate specific antigen dynamics, and other prognostic factors measured at the end of treatment, on each transition intensity between predefined clinical states. This thesis then provides statistical tools and guidelines for the computation of individual dynamic predictions of clinical events in the context of competitive risks. Finally, a last work leads to a reflection on joint modelling of longitudinal ordinal data and survival data with an innovative inference technique. To conclude, this work introduces statistical methods adapted to various types of longitudinal data and event history data, which meet the needs of clinicians. Methodological recommendations and software tools are associated with each development, for practical use by the clinical and statistical communities.
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Submitted on : Tuesday, January 23, 2018 - 2:26:09 PM
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  • HAL Id : tel-01690825, version 1



Loic Ferrer. Modélisation et prédiction conjointe de différents risques de progression de cancer à partir des mesures répétées de biomarqueurs. Médecine humaine et pathologie. Université de Bordeaux, 2017. Français. ⟨NNT : 2017BORD0875⟩. ⟨tel-01690825⟩



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