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Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the indepen...

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Autores principales: Ebrahim, Endris Assen, Cengiz, Mehmet Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046850/
https://www.ncbi.nlm.nih.gov/pubmed/35496170
http://dx.doi.org/10.3389/fpsyg.2022.855379
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author Ebrahim, Endris Assen
Cengiz, Mehmet Ali
author_facet Ebrahim, Endris Assen
Cengiz, Mehmet Ali
author_sort Ebrahim, Endris Assen
collection PubMed
description Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. The Hopkins Verbal Learning Test (HVLT) is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual’s performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo (HMC) under the Bayesian Regression Models using ‘Stan’ (BRMS) package of R. Comparisons of the fitted models were done using leave-one-out information criteria (LOO-CV), Widely applicable information criterion (WAIC), and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach.
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spelling pubmed-90468502022-04-29 Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models Ebrahim, Endris Assen Cengiz, Mehmet Ali Front Psychol Psychology Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. The Hopkins Verbal Learning Test (HVLT) is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual’s performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo (HMC) under the Bayesian Regression Models using ‘Stan’ (BRMS) package of R. Comparisons of the fitted models were done using leave-one-out information criteria (LOO-CV), Widely applicable information criterion (WAIC), and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach. Frontiers Media S.A. 2022-04-14 /pmc/articles/PMC9046850/ /pubmed/35496170 http://dx.doi.org/10.3389/fpsyg.2022.855379 Text en Copyright © 2022 Ebrahim and Cengiz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Ebrahim, Endris Assen
Cengiz, Mehmet Ali
Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
title Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
title_full Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
title_fullStr Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
title_full_unstemmed Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
title_short Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models
title_sort predicting verbal learning and memory assessments of older adults using bayesian hierarchical models
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046850/
https://www.ncbi.nlm.nih.gov/pubmed/35496170
http://dx.doi.org/10.3389/fpsyg.2022.855379
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