Bioequivalence and Bioavailability Forum

Dear Niraj,

❝ Could you please guide me how to use PowerTOST from the given link? I have never used it before. I have downloaded file from the given link.

❝

❝ We use SAS software and I guess, given link directs to R-language. Correct me if I am wrong.

You are totally right, PowerTOST is an add-on package for the R-language.
An implementation in SAS was not undertaken by me since the scaled ABE power calculations have to be based on simulations and it has been reported that the run times for such an attempt are horrible (around 1 to some days).
R is on the other hand very suitable for simulation purposes and with some clever tricks the run times are below minutes).

To use PowerTOST you must first install R. It is open source software so you don't need something like licenses or many bucks to buy. Obtain it from CRAN.

After downloading and installing R you may install PowerTOST also from CRAN via RGUI (simpler) or R-console (needs some knowledge about R-language).
In the GUI chose the menu 'Packages/Install package(s) ...' and, after choosing a CRAN mirror near to you, select package PowerTOST.

To use it for your problem try in the R-console:
library(PowerTOST) # load the package

# sample size estimation with partial replicate
# assuming intra-subject CV's of test and reference equal 30%
# and assuming a true GMR of 0.9 (pessimistic)
# will give you N=45
sampleN.RSABE(CV=0.3, theta0=0.9, design="2x3x3")

# more optimistic GMR=0.95
# will give you N=27
# (there are some small differences to the paper of the 2 Laszlo's
#  due to higher number of simulations used in sampleN.RSABE)
sampleN.RSABE(CV=0.3, theta0=0.95, design="2x3x3")

# sample size estimation with full 3-period replicate (TRT|RTR)
# according to Helmut's suggestion
# assuming intra-subject CV of test and reference equal
# will give you N=46
sampleN.RSABE(CV=0.3, theta0=0.9, design="2x2x3")

# assuming a more variable test formulation with CV=0.4
# will give you N=62
sampleN.RSABE(CV=c(0.4,0.3), theta0=0.9, design="2x2x3")

# assuming a lesser variable test formulation with CV=0.25
# will give you N=40, i.e. it pays to have a 'better' test
sampleN.RSABE(CV=c(0.25,0.3), theta0=0.9, design="2x2x3")


Hope this helps as a very short intro.
To discover more about sampleN.RSABE() type ?sampleN.RSABE in the R-console and RTFM .

And don't forget: search the forum!