ArticleScientific reports2022
Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Neuropsychological Profile and Cognitive Trajectories of Patients With Biomarker Evidence of Alzheimer Disease or Dementia With Lewy bodies.Neurology · 2026Article
- Modeling frameworks in nutritional epidemiology matter: comparing isotemporal and time-lagged Bayesian and frequentist approaches of carbohydrate intake and adiposity.Frontiers in nutrition · 2025Article
- Atrophy signature for alpha-synuclein copathology in Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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8 authors.
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Abstract
Linear mixed effects (LME) modelling under both frequentist and Bayesian frameworks can be used to study longitudinal trajectories. We studied the performance of both frameworks on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups-healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer's disease neuroimaging initiative (ADNI), and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values.
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