Longitudinal modeling using log-gamma mixed model: case of memory deterioration after chronic cerebral hypoperfusion associated with diabetes in rats

• Autor(a) principal: Ribeiro, Matheus Henrique Dal Molin
• Data de Publicação: 2019
• Outros Autores: Santiago, Amanda Nunes, Oliveira, Rubia Maria Weffort de, Milani, Humberto, Previdelli, Isolde
• 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
• topic: correlation; generalized linear mixed models; random effects; repeated measures.; Estatística Aplicada

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.