PowerTOST (version 1.1-00)

power.scABEL: (Empirical) Power for BE via scaled (widened) BE acceptance limits

Description

This function performs the power calculation of the BE decision via scaled (widened) BE acceptance limits by simulations.

Usage

power.scABEL(alpha = 0.05, theta1, theta2, theta0, CV, n, 
             design = c("2x3x3", "2x2x4"), regulator = c("EMA", "FDA"), 
             nsims = 1e+06, details = FALSE)

Arguments

alpha

Type I error probability, significance level. Conventionally mostly set to 0.05.

theta1

Lower limit for the point estimator constraint. Defaults to 0.8 if not given explicitly.

theta2

Upper limit for the point estimator constraint. Defaults to 1.25 if not given explicitly.

theta0

'True' or assumed bioequivalence ratio. Defaults to 0.95 if not given explicitly.

CV

Coefficient(s) of variation as ratio. If length(CV) = 1 the same CV is assumed for Test and Reference. If length(CV) = 2 the CV for Test must be given in CV[1] and for Reference in CV[2].

n

Number of subjects under study. May be given as vector. In that case it is assumed that n contains the number of subjects in the sequence groups. If n is given as single number (total sample size) and this number is not divisible by the number of sequ

design

Design of the study to be planned. 2x3x3 is the partial replicate design (TRR/RTR/RRT). 2x2x4 is the full replicate design with 2 sequences and 4 periods. Defaults to design="2x3x3"

regulator

Regulatory body settings for the widening of the BE acceptance limits. Defaults to design="EMA"

nsims

Number of simulations to be performed to obtain the empirical power.

details

If set to TRUE the computational time is shown as well as the components for the BE decision. p(BE-wABEL) is the probability that the CI is within widened limits. p(BE-PE) is the probability that the point estimate is within theta1 ... theta

Value

• Returns the value of the empirical power.

Details

The simulations are done via the distributional properties of the statistical quantities necessary for deciding BE based on widened ABEL.

References

Laszlo Tothfalusi and Laszlo Endrenyi "Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs" J. Pharm. Pharmaceut. Sci. (www.cspsCanada.org) 15(1) 73 - 84, 2011

See Also

sampleN.scABEL

Examples

# using all the defaults:
# design="2x3x3", EMA regulatory settings
# PE constraint 0.8-1.25, cap on widening if CV>0.5
# true ratio =0.95, 1E+6 simulations
power.scABEL(CV=0.4, n=29)
# Unbalanced design. n(i)=10/10/9 assumed.
# [1] 0.81037
# with details=TRUE to view the computational time
power.scABEL(CV=0.5, n=54, theta0=1.15, details=TRUE)
#should give
# 1e+06 sims. Time elapsed (sec):
#   user  system elapsed 
#   0.80    0.07    0.86 
# p(BE-ABE)= 0.274807 ; p(BE-wABEL)= 0.817483 ; p(BE-PE)= 0.855218 
#[1] 0.807211