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Modélisation conjointe de données longitudinales non-linéaires et de données de survie : Application au cancer de la prostate métastatique

Abstract : Treatment evaluation for metastatic Castration-Resistant Prostate Cancer (mCRPC) relies on time-to-death. Prostate-specific antigen (PSA), assumed to be linked to survival, is frequently measured. Joint modelling which consists in the simultaneous analyse of biomarker's evolution and survival is particularly adapted, but often limited to linear longitudinal process. The main objective of this PhD is to study joint modelling when biomarker kinetics is described by a nonlinear mixed-effects model (NLMEM). First, we established by simulations that the SAEM algorithm of Monolix provided unbiased parameter estimations of a nonlinear joint model, with satisfying type 1 error and power to detect a link between the two processes. Then, we developed a mechanistic joint model to characterize the relationship between PSA kinetics and survival in mCRPC patients treated by docetaxel. The structural model of the NLMEM was defined by a system of differential equations (ODEs) describing the mechanism of PSA production by docetaxel-sensitive and -resistant cells. Model selection and evaluation were detailed. The final joint model showed the predominant role of the non-observed resistant cells on survival. Lastly, we expanded tools developed in a linear context for individual dynamic prediction using nonlinear joint model. A Bayesian method provided the distribution of individual parameters. Predictive performances of the model were assessed using time-dependent discrimination and calibration metrics. These works open the way for the development of mechanistic joint models, which enable to account for the impact of several biomarkers on survival through ODEs, in order to improve therapeutic evaluation and prediction.
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Submitted on : Monday, March 6, 2017 - 5:50:00 PM
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  • HAL Id : tel-01484089, version 1

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Solène Desmée. Modélisation conjointe de données longitudinales non-linéaires et de données de survie : Application au cancer de la prostate métastatique. Statistiques [stat]. Université Paris Diderot (Paris 7) Sorbonne Paris Cité, 2016. Français. ⟨tel-01484089⟩

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