alpha... where is omega? [Power / Sample Size]

posted by Astea – Russia, 2018-02-02 22:45 (2359 d 14:39 ago) – Posting: # 18335
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Dear Smart People!

I am suffering trying to understand the problems and perspectives of alpha-adjustment.
Wherever I look I see TIE inflation...
For HVD:For well-known adaptive designs (Potvin, Xu...) TIE also behaves badly...
TIE may be also inflated by evaluating different methods for PK metrics (when one of them turns to be overpowered - as in the case of adaptive design or different CI for two Cmax and AUC).
I suspect TIE will be also inflated in studies of NTDs also (by using FDA approach or by using different confidence limits for two metrics).
Now you talk about higher-order design and dose-proportional studies...

So... Are there any ways to deal with it excepting the bright idea of iteratively adjusted alpha? While looking through the literature I've found only some suggestions of modificating ABE (with new procedures or nonlinear CI limits) or alternatives for ABE (like GSD or Two-stage designs)...

  1. Knahl SIE, Lang B, Fleischer F, Kieser M., A comparison of group sequential and fixed sample size designs for bioequivalence trials with highly variable drugs, J Clin Pharmacol, 2018, doi:10.1007/s00228-018-2415-7.
  2. Molins E., Cobo E., Ocaña J. Two-stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: Which to choose? Statistics in Medicine, V. 36, I. 30, pp 4777–4788, 2017 doi:10.1002/sim.7452.

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