Issues in use of SAS PROC.MIXED to test the significance of treatment effects in controlled clinical trials

A project that originated with the aim of documenting the implications of dropouts for tests of significance based on general linear mixed model procedures resulted in recognition of problems in the use of SAS PROC.MIXED for this purpose. In responding to suggestions and criticisms, we have further analyzed simulated clinical trial data with realistic autoregressive structure, using alternative error model formulations, different approaches to the use of covariates to model dropout patterns, and different ways to include the critical time variable in the mixed model. Results emphasize the sensitivity of the PROC.MIXED tests of significance for GROUP and TIME x GROUP equal slopes hypothesis to less than optimal modeling of the error covariance structure. Even with the authoritatively recommended best available modeling of the error structure, model formulations that made use of the REPEATED statement did not maintain conservative test sizes when covariates were required to model dropout data patterns. Random coefficients models that employed the RANDOM statement did permit appropriate covariate controls, but the tests of significance for treatment effects were lacking in power. After examining a variety of alternative PROC.MIXED model formulations, it is concluded that none provided both Type I error protection and power comparable to that of simple two-stage analysis of covariance (ANCOVA) procedures for confirming the presence of true treatment effects in controlled clinical trials. Other issues examined in this article concern treating baseline scores as both covariate and initial repeated measurement to which a linear means model is fitted, failure to take advantage of the regression of repeated measurements on time in modeling time as an unordered categorical variable, and fitting linear regression models to nonlinear response patterns.