Bioequivalence and Bioavailability Forum

Dear Captain,
I had only an idea with no connection to reality.

❝ ❝ Theoretically I can imagine (no real) data of 2x2x4 replicate study - see following idea:

The idea was to prepare data of T with intra-subject CV close to zero, for example with mean equal to 0.95 with almost zero standard deviation.
Data of R with the same properties but shifted to different mean, for example 1.05. So I expected intra-subject CV for T data close to zero and intra-subject CV for R data close to zero also. But when we pool the T and R data together, there will be higher differences in the data, so I expected the higher intra-subject CV for all (pooled) data than for T or R data separately.
So the point was completely in theoretical way with the description of example which provides the proof of higher CV_w.
Since Helmut simulated better data. - Thanks! - The description is not needed now.

❝ ❝ So GMR T/R of all "pooled" data will be close to 0.95/(1/0.95)=0.95^2=0.9025 and CV_w used for CI calculation will be higher than CV_wT and CV_wR?

❝ Does that follow automatically? How? Why? CVw used for CI calculation is a dangerous term when we talk EMA?

❝ I tried to look at Helmut's simulated data and could still not figure it out.

The statements are valid only for the crazy example which I tried to describe swiftly by guesswork. So that doesn't follow automatically. Or maybe when CV_wT = CV_wR and means of T and R differ, the CV_w will be probably slightly higher? So the equality CV_wT = CV_wR = CV_w with the infinite precision is only in a really special case of data. (added by editing)
Helmut's simulated data are much more normal than data in my mind. So I suggest to look only on Helmut's data as an example of data where CV_w is higher than CV_wT and CV_wR.

Best regards,
zizou

Note (to be deleted): typo error in Helmut's simulated data summary

❝ GLSM 0.9542 (T), 1.0480 (R), PE 0.9104.

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Edit: THX, done. [Helmut]