Relevant: The PK metric with highest CV [Power / Sample Size]
❝ 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.
Complete thread:
- Power decrease for the highly correlation of AUCt and AUCinf libaiyi 2018-05-23 08:40 [Power / Sample Size]
- Relevant: The PK metric with highest CV Helmut 2018-05-23 10:40
- Relevant: The PK metric with highest CVlibaiyi 2018-05-24 10:02
- Power of 2 independent TOSTs ≈ 2 simultaneous TOSTs with ρ=0 Helmut 2018-05-24 12:54
- Power of 2 independent TOSTs ≈ 2 simultaneous TOSTs with ρ=0 libaiyi 2018-05-30 03:24
- Power of 2 independent TOSTs ≈ 2 simultaneous TOSTs with ρ=0 Helmut 2018-05-24 12:54
- Relevant: The PK metric with highest CVlibaiyi 2018-05-24 10:02
- Relevant: The PK metric with highest CV Helmut 2018-05-23 10:40