Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35789 |
Resumo: | In recent years several longitudinal studies have been conducted in the field of pharmacology. In general, continuous response variables occur frequently in these situations and tend to present asymmetric characteristics, as well as being restricted to the set of positive real numbers. Therefore, using the normal model would be incorrect. In this conjecture, generalized linear mixed models (GLMM) are used to analyze data characterized in this way, aiming to accommodate inter- and intra-individual variations. Thus, we propose a mixed gamma model (LGMM) with a log link function and random effects normally distributed to evaluate data from a longitudinal experiment, where the effects of cerebral ischemia associated with diabetes on the performance of long-term retrograde memory were evaluated in rats. Based on the results obtained, the random intercept model presented a good fit and accommodated the correlation inherent to the data. It was possible to observe that normoglycemic animals, when compared to hyperglycemic animals, whether submitted to ischemia or not, had their cognitive capacity partially preserved, indicating that hyperglycemia (‘diabetes’) aggravates the cognitive effects of brain ischemia. |
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Acta scientiarum. Technology (Online) |
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Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in ratscorrelationgeneralized linear mixed modelsrandom effectsrepeated measures.Estatística AplicadaIn recent years several longitudinal studies have been conducted in the field of pharmacology. In general, continuous response variables occur frequently in these situations and tend to present asymmetric characteristics, as well as being restricted to the set of positive real numbers. Therefore, using the normal model would be incorrect. In this conjecture, generalized linear mixed models (GLMM) are used to analyze data characterized in this way, aiming to accommodate inter- and intra-individual variations. Thus, we propose a mixed gamma model (LGMM) with a log link function and random effects normally distributed to evaluate data from a longitudinal experiment, where the effects of cerebral ischemia associated with diabetes on the performance of long-term retrograde memory were evaluated in rats. Based on the results obtained, the random intercept model presented a good fit and accommodated the correlation inherent to the data. It was possible to observe that normoglycemic animals, when compared to hyperglycemic animals, whether submitted to ischemia or not, had their cognitive capacity partially preserved, indicating that hyperglycemia (‘diabetes’) aggravates the cognitive effects of brain ischemia.Universidade Estadual De Maringá2019-05-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3578910.4025/actascitechnol.v41i1.35789Acta Scientiarum. Technology; Vol 41 (2019): Publicação Contínua; e35789Acta Scientiarum. Technology; v. 41 (2019): Publicação Contínua; e357891806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35789/pdfCopyright (c) 2019 Acta Scientiarum. Technologyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessRibeiro, Matheus Henrique Dal MolinSantiago, Amanda NunesOliveira, Rubia Maria Weffort deMilani, HumbertoPrevidelli, Isolde2019-07-17T11:54:33Zoai:periodicos.uem.br/ojs:article/35789Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2019-07-17T11:54:33Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
title |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
spellingShingle |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats Ribeiro, Matheus Henrique Dal Molin correlation generalized linear mixed models random effects repeated measures. Estatística Aplicada |
title_short |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
title_full |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
title_fullStr |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
title_full_unstemmed |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
title_sort |
Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats |
author |
Ribeiro, Matheus Henrique Dal Molin |
author_facet |
Ribeiro, Matheus Henrique Dal Molin Santiago, Amanda Nunes Oliveira, Rubia Maria Weffort de Milani, Humberto Previdelli, Isolde |
author_role |
author |
author2 |
Santiago, Amanda Nunes Oliveira, Rubia Maria Weffort de Milani, Humberto Previdelli, Isolde |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Ribeiro, Matheus Henrique Dal Molin Santiago, Amanda Nunes Oliveira, Rubia Maria Weffort de Milani, Humberto Previdelli, Isolde |
dc.subject.por.fl_str_mv |
correlation generalized linear mixed models random effects repeated measures. Estatística Aplicada |
topic |
correlation generalized linear mixed models random effects repeated measures. Estatística Aplicada |
description |
In recent years several longitudinal studies have been conducted in the field of pharmacology. In general, continuous response variables occur frequently in these situations and tend to present asymmetric characteristics, as well as being restricted to the set of positive real numbers. Therefore, using the normal model would be incorrect. In this conjecture, generalized linear mixed models (GLMM) are used to analyze data characterized in this way, aiming to accommodate inter- and intra-individual variations. Thus, we propose a mixed gamma model (LGMM) with a log link function and random effects normally distributed to evaluate data from a longitudinal experiment, where the effects of cerebral ischemia associated with diabetes on the performance of long-term retrograde memory were evaluated in rats. Based on the results obtained, the random intercept model presented a good fit and accommodated the correlation inherent to the data. It was possible to observe that normoglycemic animals, when compared to hyperglycemic animals, whether submitted to ischemia or not, had their cognitive capacity partially preserved, indicating that hyperglycemia (‘diabetes’) aggravates the cognitive effects of brain ischemia. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-05-02 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35789 10.4025/actascitechnol.v41i1.35789 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35789 |
identifier_str_mv |
10.4025/actascitechnol.v41i1.35789 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/35789/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Acta Scientiarum. Technology https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Acta Scientiarum. Technology 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 |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 41 (2019): Publicação Contínua; e35789 Acta Scientiarum. Technology; v. 41 (2019): Publicação Contínua; e35789 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315336449753088 |