Repeated Measures Modeling with PROC MIXED
page Paper 188-29. (2004)3884<m:linebreak></m:linebreak>Mixed models; SAS; Dades longitudinals.Abstract
PROC MIXED provides a very flexible environment in which to model many types of repeated measures data, whether repeated in time, space, or both. Correlations among measurements made on the same subject or experimental unit can be modeled using random effects, random regression coefficients, and through the specification of a covariance structure. PROC MIXED provides a large variety of useful covariance structures for modeling covariation in both time and space, including discrete and continuous increments of time and space. MANOVA tests are available for some model specifications, and degrees of freedom adjustments are available to provide better approximations to the distributions of the test statistics than for standard between- or within-subject methods. The %GLIMMIX macro, available in the SAS/STAT sample library, extends the mixed model technology of PROC MIXED to generalized linear mixed models, while the %NLINMIX macro, also available in the SAS/STAT sample library, provides a similar framework for non-linear mixed models. Likelihood and information criteria are available to aid in the selection of a model when the model structure is not known a priori.