Precision of PowerTOST [Power / Sample Size]

posted by mittyri – Russia, 2017-12-09 22:05 (2377 d 13:57 ago) – Posting: # 18050
Views: 14,143

Hi Helmut,

sorry if my thoughts were unclear in previous post.
As Astea has noted, 2 Laszlos in their paper estimated the required minimum sample size as 54 subjects for "2x3x3" design (CV=0.8, theta0=0.95). They claimed that "The precision of the estimation was evaluated by running the simulations twenty times".
My question was: is it possible for PowerTOST? Is it possible to get the mean power of 80% using the mean of 20 runs?
library("ggplot2")
library("PowerTOST")
reps <- 1E4
scABELoverall <- data.frame(rep = 1:reps)
scABEL20 <- data.frame(1:20)
for(external in 1:reps){
  for(internal in 1:20){
    scABEL20$power[internal] <- power.scABEL(CV=0.8, n=54, theta0=0.95, design="2x3x3", nsims=10000, setseed=F)
  }
  scABELoverall$meanpower[external] <- mean(scABEL20$power)
}
ggplot(scABELoverall, aes(meanpower))+
  geom_density(fill = 2, alpha = 0.3)+
  theme_bw()+
  ggtitle(sprintf("%d reps: Mean is %.4f, SD is %.4f; %.2f %% of twenties are less than 0.8", reps, mean(scABELoverall$meanpower), sd(scABELoverall$meanpower), sum(scABELoverall$meanpower<0.8)/length(scABELoverall$meanpower)*100))


[image]
So the answer is: yes, that's possible, but you need to be unlucky (the probability is about 7%) :-D

Kind regards,
Mittyri

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