power is analytically accessible [Power / Sample Size]
❝ […] I have considered the intersection-union principle after reading previous posts in the forum. I was hoping to compute the predictive power for different numbers of subjects for each trial and was using bootstrapping to generate this value. Does this make more sense? Would you happen to have any resources/examples?
Of course, you could do bootstrapping but I still think that it’s over the top. For given T/R-ratio, CV, n, and design power can be directly calculated (contrary to the sample size, where you need an iterative procedure). If your are interested in some combinations, see these examples for a parallel design and a 2×2×2 crossover.
In short, whatever you want, simply set up nested loops and assign the result to a data frame (or matrix if more than two dimensions).
As a single point metric Cmax is in general the most variable one. Hence, assessing power for it should cover the other metrics as well. I like IUT.
❝ I am new to this topic and working on this project out of pure interest (forgive me if this is not the place for these questions).
That’s the best reason. It’s the right place.
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
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