## Precision of PowerTOST [Power / Sample Size]

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))

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

Kind regards,
Mittyri