Bioequivalence and Bioavailability Forum

Dear d_labes,

thanks for answering.

I will try to describe. The power given by Potvin is
P= Ft(a/(s*sqrt(2/n)) - t, DF) -  Ft(b/(s*sqrt(2/n)) - t, DF)

where a and b are simple constants, s is derived from the CV and n is the total sample size, t is the critical value given some alpha and the df (=n-2). In a 2,2,2BE crossover we have n*2 observations. DF loss:
Treatment 2 (or intercept 1 + treatment 1)
Sequence 1
Subject n-1
Period 1
and then we gain 1 due to redundancy/crossover:
So
DF₂₂₂=2n-2-1-(n-1)-1 + 1 = n-2

In a parallel study with n subjects we have n observations just lose 2 df's due to the two treatments (or one treatment plus intercept).
DF_par=n-2

Ach so....
So if power formulae (whcihever Owen's Q, shifted t, central t ...) can be ported without to Parallel studies then a parallel study and a 2,2,2-be study should have the same power for a given n, theta and s?
No. We probably need to take some kind of design constant into consideration. In this post the design constants and n were defined slightly different from the current version of power.TOST I think, at least it looks like that when I go
known.designs() in your brilliant package (installed last week).

About here here I have lost orientation. I do not know how to take the design into consideration when using Potvin's equation to calculate power for the parallel situation.

(Post changed here due to slow-working brain!)

❝ BTW: Do you really need that last "Quäntchen" for speed? I can't believe that the usage of non-central t-distribution versus central t-distribution will make much a difference in a compiler environment.

Yes I believe I need it. I can explain the trick later.