Precision of PowerTOST [Power / Sample Size]

posted by mittyri – Russia, 2017-12-08 23:56 (2123 d 19:21 ago) – Posting: # 18048
Views: 13,521

Dear Detlew,

thank you for clarification
I tried to figure out the precision of PowerTOST estimations with the following code:
reps <- 1E6
scABELdata <- data.frame(rep = 1:reps)
for(i in 1:reps){
  scABELdata$power[i] <- power.scABEL(CV=0.8, n=54, theta0=0.95, design="2x3x3", nsims=10000, setseed=F)

ggplot(scABELdata, aes(power))+
  geom_density(fill = 2, alpha = 0.3)+
  theme_bw() +
  ggtitle(sprintf("%d reps: Mean is %.4f, SD is %.4f; %.2f %% obs are less than 0.8", reps, mean(scABELdata$power), sd(scABELdata$power), sum(scABELdata$power<0.8)/length(scABELdata$power)*100))

please correct if it is wrong.
The resulted plot:

BAsed on the mean and se calculated from sd and n=20 I would say that sample mean 80% in PowerTOST is a real unfortune :cool:

❝ BTW: Both Laszlos recommend to use PowerTOST if they were asked :thumb up:.

Fully agree and thanks again for this wonderful package.

Kind regards,

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