Bioequivalence and Bioavailability Forum

Hi again again,

❝ 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().

I have come to realise* that one should be safe enough with lm for unbalanced data as long as we talk 2,2,2-BE studies, but you might want to make sure to use LSMeans as indicated in the previous post rather than means (at least if your intention is to get SAS-like results).
As soon as we talk more complex designs and/or inclusion of subjects with missing period values a switch to a mixed muddle/REML is mandatory.
To cut it all short: lme will always work for you, even with 2,2,2-studies but from a computational viewpont lme for 2,2,2-BE is somehow overkill. On a modern computer it takes a split second anyway and you never see he difference in terms of computational effort.

EM.

*: Didn't dlabes some time last year explain this? I remember I tried really hard to challenge lm with an unbalanced dataset to see if I could get it "wrong" with lm due to imbalance in a 2,2,2-study, but I failed to do that.