Tricky question, lengthy answer [Power / Sample Size]

posted by Olivbood – France, 2019-05-08 21:20 (1671 d 13:06 ago) – Posting: # 20275
Views: 6,220

Hi Helmut,

Thanks for your instructive answer !

❝ The most important CV is the one of Cmax. Here I would allow for an even larger safety margin (in steady state its variability might be substantially lower than after a SD). In other words, the lower CV in steady state dampens the pooled CV and the one you will observe after a SD likely will be higher.

❝ In the crossover you will have one degree of freedom less than in the paired design (the CI will be wider). Given, peanuts.

I see, would you know of any guidance or "good practice" on how to determine this safety margin?

❝ Now it gets nasty. In many cases the variability in fed state is (much) higher than in fasted state. I have seen too many studies of generics (sorry) where the fasting study passed (“We perform it first because that’s standard.”) – only to face a failed one in fed state. Oops! Hence, I always recommend to perform the fed study first. For you that’s tricky since you have only data of fasted state. What about a pilot or a two-stage design? I prefer the latter because with the former you throw away information. In a TSD you can stop the arms which are already BE in the first stage (likely the fasting part) and continue the others to the second stage.

That's interesting ! As far as I know no BE study is planned in the fed state (since the clinical formulation is only administered in fasting state), but I'll pass on the note.

❝ (what does “when appropriate mean”?).

No idea neither :)

❝ You only have to observe the one with the highest variability / largest deviation form unity. Yep, doable in Power.TOST. ;-)

I see, so I would only need to power the study for (supposedly) Cmax, and not use for instance the function power.2TOST to calculate the power of two TOST procedure for Cmax and AUC (either 0-inf or 0-t)?

❝ ❝ Then, since I am interested in the comparisons C vs B and A vs B, how should I proceed to get power estimates, likewise using the package Power.TOST?

❝ ❝ … so should I perform a multiplicity adjustment? Since only the marketed formulation would then be prescribed to the patients I would not use multiplicity correction, but since I plan to interpret both comparisons independently I'm not sure...

❝ Yep, that’s fine.

So, no need to perform multiplicity adjustment is that right ? I tried to find some publications on the issue but since it is neither a joint decision rule nor a multiple decision rule situation, so far no luck...

Thanks again for your help !

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