Bioequivalence and Bioavailability Forum

Dear Astea,

as far as I can see you made a lot of forum research already


I don't see any other method except direct simulations, but as Detlew mentioned in the link you provided: too many what if's!!!
Remember that you need to take into account not only 2 analytes, but 2 PK metrics for both of them.

❝ 1). Initial sample size calculation - best guess of CV for two CV?

see above - not for 2 but for 4!
Guestimation is our friend. For that particular protocol I think you can prove almost any reliable numbers. (n1 is low: well, that's a 2 Stage design, I know nothing about CV!
n1 is high: I think CV for Cmax of that analyte should go to the sky!)

❝ 2). Initial sample size calculation - power (see this thread: ). So if we would not expect independent hypothesis we should use the adjusted power for calculation. Correspondingly this will lead to neccesity of using additional simulations cause we will be automatically driven from validated values of 80 and 90%.

If you don't know CVs how would you estimate rho?
May you want to build a correlation matrix for all of 4 pk metrics?

❝ 3). Interim analyses: for 2 analytes it leads to different possibilities: pass, fail, pass for A but need the second stage for B...

Yes, end of story (again, 4 metrics!). The framework becomes absolutely crazy. So I don't see any option except independent PK metrics analysis as you did for simple analytes in 2 stage designs. Forced BE? Yes, we're gonna live with that for now...

❝ 4). Sample size for the next stage:

❝ - additional subjects could cause TIE inflation (as in the example with extra drop-outs, see this thread.

From the link provided I don't see TIE inflation (using Potvin B and Detlew's function)

❝ - is it regulatory addopted - ignoring data for the second analyte?

No, as Helmut mentioned in the same link. I don't think experts be happy trying to dive into so complicated framework.