Bioequivalence and Bioavailability Forum

Oi, this was an old thread.

❝ Possible all effects are considered fixed especially in nowadays population?

The standard 2,2,2-BE model can be fit with a linear model or a mixed model (equivalently glm, lm, lme etc in R) and you can obtain the same results.
When one uses the RANDOM statement in connection with PROC GLM, it means.......very little of relevance to the 2,2,2-B situation, as I see it. It can be omitted and we're still happy. A model with additional random effects other than the good old epsilon residuals is a mixed model, and mixed models are not linear models (they are LMS = "Linear Models on Steroids"). PROC GLM fits a linear model (only!) and in the fiting process it treats all effects as fixed regardless of the presence of a RANDOM statement.
The SAS documentation has the power to confuse: " ...the random effects are treated in a post hoc fashion after the complete fixed effect model is fit. This distinction affects other features in the GLM procedure, such as the results of the LSMEANS and ESTIMATE statements. These features assume that all effects are fixed, so that all tests and estimability checks for these statements are based on a fixed effects model, even when you use a RANDOM statement. Standard errors for estimates and LS-means based on the fixed effects model may be significantly smaller than those based on a true random effects model; in fact, some functions that are estimable under a true random effects model may not even be estimable under the fixed effects model. Therefore, you should use the MIXED procedure to compute tests involving these features that take the random effects into account."

Eh...."True random effects model"? OK, so PROC MIXED is for true random effects models, while PROC GLM is for ...what?....false random effect models, perhaps? (-which presumably are fixed because they don't contain truly random effects)? Aha, now things are very clear.
Does anyone know if there are some handy statements for nonlinear mixed effects models which are not truly linear but also not nonlinear but only sort of a little bit possibly nonlinear on a foggy day, and which can be included or omitted without consequences?

EM.