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Generalized Linear Mixed Model (GLMM) for the analysis Longitudinal Data with repeated measurements

El-Biomy Awad Awad Takia


Statistical models play an important role in the process of statistical inference in practical life in all different fields of science, and because of this role the study of statistical models have interest and continuous development to become more realistic and credible in describing the relationship between the variables of response variables and explanatory variables.
The research problem represented in many educational and medical science and biological that data are taken from the longitudinal data (data in the context of repeated measures) or the cluster data, which assumes a correlation between observations or between the same class of the unit observations of the different periods are refined as in the case of longitudinal data and, therefore, the generalized linear models, which assume the existence of independence between all the units to be observed, however, is an appropriate method for analyzing this type of data.
The applied area of this study was the clinical study which consists of sample of 126 patients, who were treated at Urology and Nephrology Center-Mansoura University, and we assessed renal function by measuring Serum Creatinine through 4 visits, then we can calculated GFR and urine albumin which allow early detection of Chronic Kidney disease.
In this study the method of Generalized Linear Mixed Models will be used as appropriate method for the analysis of longitudinal data with repeated measurements, which take into account the existence of a correlation between repeated measurements for the same individual, through the integration of the Random Effects with Fixed Effects in the previous models.


Generalized Linear Mixed Model (GLMM), Random Effects, Fixed Effects, Longitudinal data, Robust Estimation

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