Relevant: The PK metric with highest CV [Power / Sample Size]

posted by libaiyi – China, 2018-05-24 10:02  – Posting: # 18811
Views: 1,356

» You could use function power.2TOST() of the R-package PowerTOST to explore various correlations (ρ). This issue is a little bit academic because ρ is rarely known.
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  • An early truncated partial AUC can be highly variable as well.

Hi Helmut,

Thank you for the reply. I am afraid that I did not state clearly. I still want to clarify do you mean that for the estimation of sample size, the power need to be calculated as:
Overall power = (power of AUC0 * power of AUCinf * power of Cmax)
like 0.8=(0.92*0.92*0.92)
And it could not be simplified as Overall power = (power of AUC0 * power of Cmax) to decrease power needed of each for the lack of ρ?

Thanks again.

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