Precision of PowerTOST [Power / Sample Size]

posted by mittyri – Russia, 2017-12-08 23:56 (2328 d 02:13 ago) – Posting: # 18048
Views: 13,928

Dear Detlew,

thank you for clarification
I tried to figure out the precision of PowerTOST estimations with the following code:
library("ggplot2")
library("PowerTOST")
reps <- 1E6
scABELdata <- data.frame(rep = 1:reps)
for(i in 1:reps){
  set.seed(i)
  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:
[image]

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,
Mittyri

Complete thread:

UA Flag
Activity
 Admin contact
22,993 posts in 4,828 threads, 1,655 registered users;
70 visitors (0 registered, 70 guests [including 8 identified bots]).
Forum time: 03:10 CEST (Europe/Vienna)

So far as I can remember,
there is not one word in the Gospels
in praise of intelligence.    Bertrand Russell

The Bioequivalence and Bioavailability Forum is hosted by
BEBAC Ing. Helmut Schütz
HTML5