Analysis Of Mixed-effect Model Based On Longitudinal Data

Research on longitudinal data is widely used in medical science and social science, models of longitudinal data has also been a popular project in statistics.This passage introduces basis of longitudinal data and the general model of longitudinal data:mixed-effect model and the estimation of the parameters.In many situations,joint modeling of multivariate longitudinal profiles is needed or has additional advantages over the separate analysis of different outcomes.The association structure can be of importance. A possible ques-tion might be how the association between outcomes evolves over time or how outcome-specific evolutions are related to each other. However, compu-tational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes increases.We pro-pose a pairwise approach in which all possible bivariate models are fitted,in the latter case,estimation of the parameters can be obtained by averaging all corresponding pair-specific estimates.In the last,we describe one example using the pairwise approach.Through the comparison between joint modeling and pairwise approach,we can dis-cuss which method is better in different situations.In this way,we can choose appropriate method in different situations so that we can avoid the compu-tational problem as well as obtain the precise estimation.