The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing

Detalhes bibliográficos
Autor(a) principal: Costa, Patrício Soares
Data de Publicação: 2013
Outros Autores: Santos, Nadine Correia, Cunha, Pedro, Palha, Joana Almeida, Sousa, Nuno
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/25595
Resumo: The main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.
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spelling The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageingScience & TechnologyThe main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.The study is integrated in the "Maintaining health in old age through homeostasis (SWITCHBOX)" collaborative project funded by the European Commission FP7 initiative (grant HEALTH-F2-2010-259772). NS and JAP are main team members of the European consortium SWITCHBOX (http://www.switchbox-online.eu/). NCS is supported by a SwitchBox post-doctoral fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.PLOSUniversidade do MinhoCosta, Patrício SoaresSantos, Nadine CorreiaCunha, PedroPalha, Joana AlmeidaSousa, Nuno2013-082013-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/25595eng1932-620310.1371/journal.pone.007194023977183http://www.plosone.org/info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:22:27Zoai:repositorium.sdum.uminho.pt:1822/25595Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:15:56.738066Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
title The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
spellingShingle The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
Costa, Patrício Soares
Science & Technology
title_short The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
title_full The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
title_fullStr The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
title_full_unstemmed The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
title_sort The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing
author Costa, Patrício Soares
author_facet Costa, Patrício Soares
Santos, Nadine Correia
Cunha, Pedro
Palha, Joana Almeida
Sousa, Nuno
author_role author
author2 Santos, Nadine Correia
Cunha, Pedro
Palha, Joana Almeida
Sousa, Nuno
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Costa, Patrício Soares
Santos, Nadine Correia
Cunha, Pedro
Palha, Joana Almeida
Sousa, Nuno
dc.subject.por.fl_str_mv Science & Technology
topic Science & Technology
description The main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.
publishDate 2013
dc.date.none.fl_str_mv 2013-08
2013-08-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/25595
url http://hdl.handle.net/1822/25595
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1932-6203
10.1371/journal.pone.0071940
23977183
http://www.plosone.org/
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv PLOS
publisher.none.fl_str_mv PLOS
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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