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Poster Année : 2022

Covariate-Aware Longitudinal Modelling for Neurodegenerative Diseases

Résumé

Longitudinal modelling is of pivotal interest for the study of neurodegenerative diseases. The Disease Course Mapping is a multivariate Bayesian mixed-effect progression model that is able to recover the course of a disease from a cohort with multimodal longitudinal observations (imaging variables, cognitive and clinical scores) and to extract interpretable parameters to describe each patient. It has been validated on multiple diseases and on multiple applications settings (cohort study, trial enrichment, data simulation, ...) However, its statistical formulation relies only on time-dependent observations. It thus fail to integrate time-independent information (gender, education levels, genetic factors, ...) in its modelling, even though such covariates are known to modulate clinical disease courses. We propose a mixed-effect formulation that captures the influence of such covariates over the dynamic of the disease.
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Dates et versions

hal-03832976 , version 1 (28-10-2022)

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  • HAL Id : hal-03832976 , version 1

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Nemo Fournier, Stanley Durrleman​. Covariate-Aware Longitudinal Modelling for Neurodegenerative Diseases. 43rd Annual Conference of the International Society for Clinical Biostatistics (ISCB), Aug 2022, Newcastle, United Kingdom. . ⟨hal-03832976⟩
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