Evaluating the Performance of Bayesian and Frequentist Approaches for Longitudinal Modeling: Application to Alzheimer's Disease

Overview

Journal Sci Rep

Specialty Science

Date 2022 Aug 24

PMID 36002550

Authors

Agnes Perez-Millan

Jose Contador

Raul Tudela

Aida Ninerola-Baizan

Xavier Setoain

Albert Llado

Raquel Sanchez-Valle

Roser Sala-Llonch

Affiliations

Soon will be listed here.

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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