Application of longitudinal data for multilevel models approach on diabetes mellitus disease

Author : Wudneh ketema moges

Keyword : Diabetes, multilevel, longitudinal ,anova , macova.

Subject : Diabetes

Article Type : Original article (research)

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Abstract : As multilevel models, hierarchical models and individual growth models increase in popularity, the need for credible and flexible software that can be used to fit them to data increases. To determine the prevalence of diabetic mellitus patients and identify associated risk factors using multilevel longitudinal model and understand multilevel model changes for level-1 and level-2 models. Multilevel models were developed for analyzing hierarchically structured data. Some examples of hierarchically structured data are students nested within schools and employees nested within companies. The large proportion of the variability in the by follow up time explained by diabetes mellitus emphasizes the importance of accounting for the hierarchical structure of the data. The fixed effects 𝛾̂00and 𝛾̂10 estimates the starting point(y-intercept) and slope of the population average change trajectory for time points. The parameters are significant (t-value of 206.52 and 0.75 respectively) which indicates that they should both be included in the model. For a two-level longitudinal MLM, the software requires input of values regarding the duration of the study, frequency of observations, the level 1 variance , the between –person variability in the parameter of interest and an estimates of the effect size

Article by : Wudneh Ketema Moges

Article add date : 2021-08-31

How to cite : Wudneh ketema moges. (2021-August-31). Application of longitudinal data for multilevel models approach on diabetes mellitus disease. retrieved from https://www.openacessjournal.com/abstract/832