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Poster Année : 2023

A multivariate bayesian mixed-effect model (Leaspy) to analyze the trajectory of cognitive decline in CADASIL

Un modèle bayésien multivarié à effets mixtes (Leaspy) pour analyser la trajectoire du déclin cognitif dans CADASIL

Résumé

Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), the most frequent cerebral small artery disease is caused by stereotyped mutations of the NOTCH3 gene. The multifaceted clinical presentation of this disorder can be assessed using various measures. We used a bayesian mixed effect model(Leaspy) to model the multivariate disease progression and explore the spatiotemporal relationships between these measures. We were also able to identify different groups of individual evolution according to the time shift and acceleration rate by taking into account the gender, education level, smoking, hypertension and the mutation's position.
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Dates et versions

hal-04399800 , version 1 (17-01-2024)

Identifiants

  • HAL Id : hal-04399800 , version 1

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Sofia Kaisaridi, Hugues Chabriat, Sophie Tezenas Du Montcel. A multivariate bayesian mixed-effect model (Leaspy) to analyze the trajectory of cognitive decline in CADASIL. 44th Annual Conference of the International Society for Clinical Biostatistics, Aug 2023, Milan, Italy. ⟨hal-04399800⟩
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