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

Hi libaiyi,

❝ I want to ask about power calculation. In BE, Cmax, AUCt, and AUCinf are all needed for power consideration. […] But AUCinf and AUCt are highly correlated,

AUC and Cmax are (highly?) correlated as well.

❝ […] so could I […] only consider about AUCt and Cmax for the power setting?

If you think about sample size estimation I would go a step further and consider only the PK metric with the highest variability, which generally* is the one of Cmax (see [msg]this thread[/msg], linked other posts, and references). 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.

• An early truncated partial AUC can be highly variable as well.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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