## Output of sampleN.scABEL() [Power / Sample Size]

» But why the 90% BE range is still 80.00-125.00% where you know that you can apply the HVD concept to widen the boundary?

Seemingly the output of the function

`sampleN.scABEL()`

is not clear enough. It gives:`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

*expanded*limits, the point estimate has to lie within 80.00–125.00%.

See this article for the decision scheme.

In coding the function

`sampleN.scABEL.ad()`

I tried to be more specific (see also my post below) and it gives for \(\small{CV_\textrm{wT}=CV_\textrm{wR}=0.3532}\):`Regulatory settings: EMA (ABEL)`

Switching CVwR : 0.3

Regulatory constant: 0.76

Expanded limits : 0.7706 ... 1.2977

Upper scaling cap : CVwR > 0.5

PE constraints : 0.8000 ... 1.2500

`Regulatory settings: EMA (ABE)`

Switching CVwR : 0.3

BE limits : 0.8000 ... 1.2500

Upper scaling cap : CVwR > 0.5

PE constraints : 0.8000 ... 1.2500

Note that the sample size tables of the ‘The Two Lászlós’* don’t reach below \(\small{CV_\textrm{wR}=30\%}\). They state:

*»In view of the consequences of the mixed approach, it could be judicious to consider larger numbers of subjects at variations fairly close to 30%.«*

You could assess at which \(\small{CV_\textrm{wR}}\) (on the average) we will switch in the simulations.

`library(PowerTOST)`

fun <- function(x) {

n.1 <- sampleN.TOST(CV = x, theta0 = theta0,

targetpower = target,

design = design, details = FALSE,

print = FALSE)[["Sample size"]]

n.2 <- sampleN.scABEL(CV = x, theta0 = theta0,

targetpower = target,

design = design, details = FALSE,

print = FALSE)[["Sample size"]]

return(n.1 - n.2)

}

theta0 <- 0.90

target <- 0.80

design <- "2x3x3"

cat(design, "design:",

sprintf("Equal sample sizes for ABE and ABEL at CV = %.2f%%.",

100 * uniroot(fun, interval = c(0.3, 0.4), extendInt = "yes")$root), "\n")

2x3x3 design: Equal sample sizes for ABE and ABEL at CV = 27.90%.

- Tóthfalusi L, Endrényi L.
*Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs.*J Pharm Pharmaceut Sci. 2011; 15(1): 73–84. doi:10.18433/j3z88f. Open access.

*Dif-tor heh smusma*🖖

_{}

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

Science Quotes

### Complete thread:

- Sample size and Replicated studies Bebac user 2022-03-23 09:38 [Power / Sample Size]
- Sample size and Replicated studies dshah 2022-03-23 11:30
- Output of sampleN.scABEL()Helmut 2022-03-23 12:43
- Output of sampleN.scABEL() - expanded limits ? d_labes 2022-03-23 16:33
- Output of sampleN.scABEL() - expanded limits ? Helmut 2022-03-23 16:45

- Output of sampleN.scABEL() dshah 2022-03-23 18:08
- Don’t use FARTSSIE for SABE Helmut 2022-03-23 19:42

- Output of sampleN.scABEL() - expanded limits ? d_labes 2022-03-23 16:33

- Output of sampleN.scABEL()Helmut 2022-03-23 12:43
- Sample size larger than clinical capacity Helmut 2022-03-23 11:43
- Sample size and Replicated studies Brus 2022-03-23 14:25

- Sample size and Replicated studies dshah 2022-03-23 11:30