The Application Of Mixed Effects Model In The Analysis Of Multiple Responses Repeated Measurement Data

Repeated measurement data is very common in the medical study.The analyzing method of this kind of data have been improved a lot after the development of many years,especially by fitting mixed effects models and using the MIXED procedure of SAS software to analyze,comparing to the general multivariate analysis and the GLM procedure of SAS,the model is more flexible and the result is more credible.But in many papers,the use of mixed effects model is in the single variable repeated measurement and the correlation is just between multiple repeated measure of one variable.In many clinical diagnosis or experimental therapy and other sciences,we usually encount the repeated measures of two or more variables.The variables are not independent.For example,in the blood pressure measurement of hypertention,each patient's systolic pressure(SP) and diastolic pressure(DP) are measured three times,the three measurements of SP or DP are correlated and the two variables of each time are correlated tool In many papers,this kind of bivariate repeated measure data was dealt according single variable repeated measure or general single variable analysis,in fact,these methods are not accurate.At the same time,the correlation of multivariate repeated measure data can be cut ino two parts:between multiple measurements and between variables,and the random error also can be divided into repeated measure error in the subject and the error between subjects.On the basis of mixed effects models,the study according to the characteristics of multiple reponses repeated measurement data,uses the mixed effects models and MIXED procedure to analyze the data,not only to mine the infomation and obtain the eastimate of fixed effect and random effect,but also to study the correlation in the data and get the correlation coefficient between variables and between multiple repeated measurement.In addition,analysts can fit different mixed effects models to select the best one by comparing fit statistics and the number of covariance parameters.The result indicates that the mixed effects model can be applied into the analysis of multiple reponses repeated measurement data and the analyst can obtain more credible and detailed results comparing single variable analysis.It utilizes the infomation more sufficiently and could be applied in practice in future.