xpresso
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United States,
2023-09-19 15:40
(163 d 07:10 ago)

Posting: # 23720
Views: 1,415
 

 Sequential Sample Size and Power Estimate in R [Power / Sample Size]

Hi there! I am looking for insight into how to calculate power and sample size estimation for separate PK endpoints (Auclast, Aucinf, etc.). In particular, I am looking to randomly sample different sample sizes and estimate the power to pass for bioequivalence for each given sample size. For example, with 30 subjects (15 per arm) the power for this subset of samples to pass for bioequivalence would be ___ %.


I have already simulated the 1:1 parallel trial of 1,000 individuals and repeated the trial 10,000 times (denoted by the rep column below). I have been using the Rxode2 package in R to simulate, with wanting to calculate the power and sample size estimation based on their "Weight-based dosing example", seen here : https://nlmixrdevelopment.github.io/RxODE-manual/examples.html.

Below is what the dataset currently looks like:
[image]
Helmut
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Vienna, Austria,
2023-09-19 16:04
(163 d 06:47 ago)

@ xpresso
Posting: # 23721
Views: 1,128
 

 IUT

Hi xpresso & welcome to the club!

I think that you are overdoing it.
If you have multiple PK metrics and all must pass (that’s the regulatory requirement) at level \(\small{\alpha}\) – in general 0.05 – you are protected against inflation of the type I error by the intersection-union principle.1 No need for simulations,2 simply calculate power for given T/R-ratio, CV, and the sample size. If any of the metrics shows power <50%, the study will fail (see this article).


  1. Berger RL, Hsu JC. Bioequivalence Trials, Intersection-Union Tests and Equivalence Confidence Sets. Stat Sci. 1996; 11(4): 283–302. JSTOR:2246021.
  2. Zeng A. The TOST confidence intervals and the coverage probabilities with R simulation. March 14, 2014. Online.

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xpresso
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United States,
2023-09-19 16:18
(163 d 06:32 ago)

@ Helmut
Posting: # 23722
Views: 1,131
 

 IUT

❝ Hi xpresso & welcome to the club!

Hi! Thank you for the warm welcome and response! I have been privately reading the materials in the forum over the past couple months and finally made an account.

❝ I think that you are overdoing it. […]

This is good to know! 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? 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). Thank you for your help :-D


Edit: Full quote removed. Please delete everything from the text of the original poster which is not necessary in understanding your answer; see also this post #5[Helmut]
Helmut
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Vienna, Austria,
2023-09-19 17:46
(163 d 05:05 ago)

@ xpresso
Posting: # 23723
Views: 1,133
 

 power is analytically accessible

Hi xpresso

❝ […] 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.

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