Joint modeling to predict the probability of progression from mild cognitive impairment to Alzheimer’s disease
Joint modeling to predict the probability of progression from mild cognitive impairment to Alzheimer’s disease
Joint longitudinal-survival models are useful when repeated measures and event times are available and possibly associated.
Different types of data for each subject at multiple timepoints leading up to a conversion event, such as the conversion from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD). These data, obtained from the ADNI (Alzheimer’s Disease Neuroimaging Initiative) datasets, include known biomarkers related to amyloidosis and neurodegeneration such as CSF ABeta 1-42, CSF tau, FDG-PET, hippocampal volumes (HV) measured through MRI, as well as neuropsychological test scores like ADAS-Cog and MMSE.
The goal of this thesis is to investigate the association between longitudinal biomarker (eg. HV) and event of interest (eg. Conversion to AD), and subsequently employ this association to predict the time of conversion for new subjects.
Many published articles have demonstrated this association, but not explicitly indicate the risk probability of progression. Separate analysis of longitudinal biomarkers and time-to-conversion may lead to inefficient or biased results. Joint models for longitudinal and survival data trait information simultaneously and provide valid and efficient inferences (L.Wu et al., 2011).
Sont éligibles les candidat(e)s ayant un diplôme de Master / d’ingénieur (ou équivalent) en intelligence artificielle, statistique, biostatistique, biologie computationnelle ou informatique (analyse de données massives) avec maîtrise du logiciel R et/ou Python et ayant une expérience en modélisation statistique dans le domaine de la santé (considéré comme un atout). De plus, le candidat doit être bon en anglais.
Procédure d'inscription
- Dossier de candidature : Curriculum vitae, lettre de motivation, projet de fin d’études, diplômes et relevés de notes
- Conditions de travail : Le doctorant mènera des travaux de recherche à plein temps à l’Université Euromed de Fès
Dossier à envoyer à a.mouiha@ueuromed.org avant le 10 novembre 2023