Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease

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 (H...

Descripción completa

Detalles Bibliográficos
Autores: Pérez Millan, Agnès, Contador Muñana, José Miguel, Tudela Fernández, Raúl, Niñerola Baizán, Aida, Setoain Perego, Xavier, Lladó Plarrumaní, Albert, Sánchez Valle, Raquel, Sala Llonch, Roser
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/202186
Acceso en línea:https://hdl.handle.net/2445/202186
Access Level:acceso abierto
Palabra clave:Malaltia d'Alzheimer
Trastorns de la memòria
Imatges per ressonància magnètica
Diagnòstic per la imatge
Alzheimer's disease
Memory disorders
Magnetic resonance imaging
Diagnostic imaging
id ES_e283f643869b3283fdff6bca31c1eb80
oai_identifier_str oai:diposit.ub.edu:2445/202186
network_acronym_str ES
network_name_str España
repository_id_str
spelling Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's diseasePérez Millan, AgnèsContador Muñana, José MiguelTudela Fernández, RaúlNiñerola Baizán, AidaSetoain Perego, XavierLladó Plarrumaní, AlbertSánchez Valle, RaquelSala Llonch, RoserMalaltia d'AlzheimerTrastorns de la memòriaImatges per ressonància magnèticaDiagnòstic per la imatgeAlzheimer's diseaseMemory disordersMagnetic resonance imagingDiagnostic imagingLinear 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.Nature Publishing Group2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/202186Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1038/s41598-022-18129-4Scientific Reports, 2022, vol. 12, num. 1, p. 14448https://doi.org/10.1038/s41598-022-18129-4cc-by (c) Pérez Millán, Agnès et al., 2022https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2021862026-05-27T06:46:51Z
dc.title.none.fl_str_mv Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
title Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
spellingShingle Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
Pérez Millan, Agnès
Malaltia d'Alzheimer
Trastorns de la memòria
Imatges per ressonància magnètica
Diagnòstic per la imatge
Alzheimer's disease
Memory disorders
Magnetic resonance imaging
Diagnostic imaging
title_short Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
title_full Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
title_fullStr Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
title_full_unstemmed Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
title_sort Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease
dc.creator.none.fl_str_mv Pérez Millan, Agnès
Contador Muñana, José Miguel
Tudela Fernández, Raúl
Niñerola Baizán, Aida
Setoain Perego, Xavier
Lladó Plarrumaní, Albert
Sánchez Valle, Raquel
Sala Llonch, Roser
author Pérez Millan, Agnès
author_facet Pérez Millan, Agnès
Contador Muñana, José Miguel
Tudela Fernández, Raúl
Niñerola Baizán, Aida
Setoain Perego, Xavier
Lladó Plarrumaní, Albert
Sánchez Valle, Raquel
Sala Llonch, Roser
author_role author
author2 Contador Muñana, José Miguel
Tudela Fernández, Raúl
Niñerola Baizán, Aida
Setoain Perego, Xavier
Lladó Plarrumaní, Albert
Sánchez Valle, Raquel
Sala Llonch, Roser
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Malaltia d'Alzheimer
Trastorns de la memòria
Imatges per ressonància magnètica
Diagnòstic per la imatge
Alzheimer's disease
Memory disorders
Magnetic resonance imaging
Diagnostic imaging
topic Malaltia d'Alzheimer
Trastorns de la memòria
Imatges per ressonància magnètica
Diagnòstic per la imatge
Alzheimer's disease
Memory disorders
Magnetic resonance imaging
Diagnostic imaging
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/202186
url https://hdl.handle.net/2445/202186
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1038/s41598-022-18129-4
Scientific Reports, 2022, vol. 12, num. 1, p. 14448
https://doi.org/10.1038/s41598-022-18129-4
dc.rights.none.fl_str_mv cc-by (c) Pérez Millán, Agnès et al., 2022
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Pérez Millán, Agnès et al., 2022
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422392740151296
score 15.300719

Similares en LA Referencia