Mixed-model pairwise multiple comparisons of repeated measures means

One approach to the analysis of repeated measures data allows researchers t o model the covariance structure of the data rather than presume a certain structure, as is the case with conventional univariate and multivariate, te st statistics. This mixed-model approach was evaluated for testing all poss ible pairwise differences among repeated measures marginal means in a Betwe en-Subjects x Within-Subjects design. Specifically, the authors investigate d Type I error and power rates for a number of simultaneous and stepwise mu ltiple comparison procedures using SAS (1999) PROC MIXED in unbalanced desi gns when normality and covariance homogeneity assumptions did not hold. J. P. Shaffer's (1986) sequentially rejective step-down and Y. Hochberg's (198 8) sequentially acceptive step-up Bonferroni procedures, based on an unstru ctured covariance structure, had superior Type I error control and power to detect true pairwise differences across the investigated conditions.