Bioequivalence and Bioavailability Forum

Dear D. Labes,

Please allow me to quickly and briefly respond this message.

❝ My interpretation of the code is: The MSE is taken from the lm() call, but

❝ point estimate is calculated by the ordinary means m_T and m_R.

❝ Any body out there: Am I right?

Exactly.

❝ If I am right: This is ok, as long as the design is balanced between

❝ sequences. but for unbalanced data there is a difference

We have discussed this back to previous versions. Indeed, we should use lme() (counterpart to SAS PROC MIXED) to analyze unbalanced data, not using lm() here. We forget to take care of this part so far. Many thanks for reminding us. We need to write a conditional statement when doing 2x2x2 crossover design to separate the analysis of unbalanced data using lme().

❝ Astonishing enough there seems no counterpart to LSMeans in R. After

❝ reading tons of Web pages it seems this another mine field like Type III

❝ sums of squares.

Exactly, just like replied message by ElMaestro. In R, there is no counterpart to LSMeans.

❝ But nevertheless there is the task to estimate the treatment effect with

❝ 90% confidence interval (from the parameter of the fitted model), i.e.

❝ from a counterpart of the ESTIMATE statement.

In R, I think lme() should be able to solve the problem easily and nicely. We have previously validated the results done with lme() for unbalanced data compared with SAS PROC MIXED. We got the same. Unfortunately, we just forgot to implement this into bear at that moment.

❝ Dear Bears have a look on to this page.

❝ Eventually this helps you. I don't understand it, because I am

❝ a novice .

Thanks for your pointer.