# Bioequivalence and Bioavailability Forum 04:28 CET

## Power and type I adjustment for 222BE when several PK prams considered [General Sta­tis­tics]

Hi all,

can we take one step back and discuss what we are really trying to achieve here? Forget for a moment the famous theorems and equations and think practically.

Ratios of Cmax and AUC are correlated within studies. No doubt about it. Increase Cmax and AUC increases, all other factors equal, and vice versa.
It would perhaps be good if we could some model that via some rho so that we could calculate a slightly higher sample size to take into consideration that both ratios must pass the 80.00%- 125.00% criterion.

But I never heard a sponsor or CRO really express that need in a specific manner. I never saw a study failing on one metric and passing on the other and where someone afterwards thought she/he should have taken the correlation into consideration and all would have been good.

Bear in mind also that both test and reference batches of any product have variation within and between. The latter is almost never explored but it is generally higher than within, as usual. Both, whether estimable/measurable or not, will be reflected in the rho we can observe and thus we may have to take that into consideration too.
The exact same issue applies to the papers that try to express a general opinion about product A vs product C, when product A has been tested against product B, and B has been tested against C. It is a matter authors have managed to tiptoe elegantly around and certainly not something anyone wants to get mixed up in.

What I mean here is I am in doubt if we are discussing a practical problem. And secondly if this problem is practical, I am wondering if the treatment we can realistically give the issue changes much given all the underlying complexity.

``` if (3) 4 x=c("Foo", "Bar") b=data.frame(x) typeof(b[,1]) ##aha, integer? b[,1]+1 ##then let me add 1 ```

Best regards,
ElMaestro

"(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures." New York Times (ed.), June 9, 2018.

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