Bioequivalence and Bioavailability Forum

Hi BEQool,

❝ thank you for a well-explained answer with specific examples.

Welcome. No big deal because I had the simulation-script already.

❝ Doesnt a model with subject as a fixed effect automatically exlude subjects with missing periods (in 2x2x2 design) from the analysis? Or am I wrong?

Of course, you are right.

❝ If I am not wrong and if I understand correctly, based on your examples with incomplete data (periods missing), the difference between subject as a fixed effect and subject as a mixed effect is more than a slight difference (instead the difference is observed in the 1st decimal place)?

Yes. That’s why I wrote:

❝ ❝ There are differences for incomplete data (period missing) – not only for the CI but also the PE.

Note also that the width of CI by the mixed effect models is generally (a little bit) narrower than the one by the fixed effect model because some information is recovered. Peanuts.

❝ Yes I was most interested in these incomplete data. So basically if you have incomplete data set (just periods missing) you should exclude the subject from the analysis and then the result (PI and 90%CI) would be almost the same (regardless of subject as a fixed or mixed effect)? Furthermore, this result would again be almost the same as if we wouldnt exclude the subject from the analysis and just used subject as a fixed effect?

Yep.

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P.S.: I modified the script in such a way that you can specify the number of dropouts and subjects with missings. Try this:

CV         <- 0.15
theta0     <- 0.975
target     <- 0.90
alpha      <- 0.05
per.effect <- 0
carryover  <- c(0, 0)
dropouts   <- 2
missings   <- 1

Interesting, isn’t it?