ABEL ≠ RSABE [Outliers]

posted by BEQool  – 2023-10-09 16:47 (284 d 22:11 ago) – Posting: # 23747
Views: 2,669

Hello!

I have seen this or similar explanation several times:

❝ If you design studies for 80% power and all of your assumptions are exactly realized (T/R-ratio, CV, dropout-rate), one out of five will fail be pure chance. That’s life.


... but I still can't get my head around this.

Lets say I plan study with theta0=0.95, CV=0.20, design="2x2", targetpower=0.8 (all except CV are default settings in PowerTOST), so with sampleN.TOST I get N=20 and power=0.834680
> sampleN.TOST(theta0=0.95, CV=0.20, design="2x2", targetpower=0.8, print=FALSE)
  Design alpha  CV theta0 theta1 theta2 Sample size Achieved power Target power
    2x2  0.05   0.2  0.95    0.8   1.25          20      0.8346802          0.8


If all of my assumptions in a study are exactly realized, doesnt it mean that I would 100% always get bioequivalent formulations (and the study would never fail)?
If all of my assumptions in a study are exactly realized (pe=0.95, CV=0.20, design="2x2", n=20) then I would get the following confidence interval:
> CI.BE(pe=0.95,CV=0.20,n=20)
    lower     upper
0.8522362 1.0589787


So because a confidence interval is completely within limits 80-125%, my formulations would always be bioequivalent because I would always get this confidence interval with same numbers (CV,n,pe)?

What am I not understanding here?
Thank you and best regards
BEQool

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