PROC MIXED has become a standard tool for analyzing repeated measures data. Its popularity results from a wide choice of correlated error models compared to other software, e.g. PROC GLM. However, PROC MIXED's versatility comes at a price. Users must take care. Problems may result from MIXED defaults. These include: questionable criteria for selecting correlated error models; starting values that may impede REML estimation of covariance components; and biased standard errors and test

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... Problems may be induced by inadequate design. This paper is a survey of current knowledge about mixed model methods for repeated measures. Examples are presented using PROC MIXED to demonstrate these problems and ways to address them. ~ Once an appropriate covariance model is chosen, how accurately does PROC MIXED compute test-statistics, degrees of freedom, standard errors, p-values, etc. Linear Mixed Model Results a. Model, estimation, and inference The repeated measures model equation (2.1) is a special case of the mixed model y=XP +ZU +e