sampleN.TOST vs. sampleN.scABEL [Power / Sample Size]

Hello!

I have searched the forum but couldn't find the answer to the following question: why does the sample size estimation with R with package PowerTOST differ between sampleN.TOST and sampleN.scABEL when CV=30%?

Lets take a look at the following example:

a) Sample size estimation with sampleN.TOST
sampleN.TOST(CV=0.3, theta0=0.95, design="2x3x3")

+++++++++++ Equivalence test - TOST +++++++++++
Sample size estimation
-----------------------------------------------
Study design: 2x3x3 (partial replicate)
log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.8
BE margins = 0.8 ... 1.25
True ratio = 0.95,  CV = 0.3
Sample size (total)
n     power
30   0.820400

b) Sample size estimation with sampleN.scABEL
sampleN.scABEL(CV=0.3, theta0=0.95, design="2x3x3")

+++++++++++ scaled (widened) ABEL +++++++++++
Sample size estimation
(simulation based on ANOVA evaluation)
---------------------------------------------
Study design: 2x3x3 (partial replicate)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.3; CVw(R) = 0.3
True ratio = 0.95
ABE limits / PE constraint = 0.8 ... 1.25
EMA regulatory settings
- CVswitch            = 0.3
- cap on scABEL if CVw(R) > 0.5
- regulatory constant = 0.76
- pe constraint applied
Sample size search
n     power
24   0.7814
27   0.8257

So why do the sample size estimations differ? They have the same arguments (design="2x3x3", theta0=0.95 ...). CV is 30% so there should be no scaling (conventional BE limits, i.e., 80.00-125.00). Does it have to do anything with simulations? But even when I increase number of simulations, the differences aren't that big.

Or if I reformulate the question, why dont the following powers match:

a) power.TOST(CV=0.3, theta0=0.95,design="2x3x3", n=30)
[1] 0.8204004

b) power.scABEL(CV=0.3, theta0=0.95, design="2x3x3", n=30)
[1] 0.85977

Regards
BEQool

Edit: Category changed. [Helmut]