Longitudinal Data Analysis And Application Random Effect

A longitudinal data analysis refers to a research where the results of the partic-ipant after exposures or possible treatment are collected in multiple follow-up times. The longitudinal study arises in a response of the participant and is measured at several occasions on the same unit. In longitudinal study, mixed effect models are beneficial for treating subject-specific change. The mixed effect model explains a relationship between a response variable and covariate which have been measured with response. The term mixed effect model refers to the model that contains fixed and random effect model. Liner mixed effect regression model is useful in longitudi-nal analyzing because it permits investigator to examine response with more flexible model. The three sources of variability:between-individual heterogeneity, within-individual biological variation, and measurement error, for longitudinal study have effect on correlation among repeated measures. The main purposes of longitudinal data analysis are to examine how the response changes over the time, and to define which factors effect the change in response.