PowerTOST (version 1.3-5)

power.RSABE: (Empirical) Power for BE decision via linearized scaled ABE criterion

Description

This function performs the power calculation of the BE decision via linearized scaled ABE criterion by simulations.

Usage

power.RSABE(alpha = 0.05, theta1, theta2, theta0, CV, n, 
            design = c("2x3x3", "2x2x4", "2x2x3"), regulator = c("FDA", "EMA"),
            nsims = 1e+05, details = FALSE, setseed=TRUE)

Arguments

alpha

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

theta1

Conventional lower ABE limit to be applied in the mixed procedure if CVsWR

theta2

Conventional upper ABE limit to be applied in the mixed procedure if CVsWR

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. 2x2x3 is the 3-period design with sequences TRT|RTR. Defaults to design="2x3x3".

regulator

Regulatory body settings for the scaled ABE criterion. Defaults to regulator="FDA". Also the linearized scaled ABE criterion is usually calculated with the FDA constant r_const=log(1.25)/0.25 you can override this behavior to use the EMA

nsims

Number of simulations to be performed to obtain the empirical power. Defaults to 100 000 = 1e+5. If simulations are aimed for empirical alpha nsims=1e+06 is recommended.

details

If set to TRUE the computational time is shown as well as the components for the BE decision. p(BE-sABEc) is the probability that the 95% CI of the ABE criterion is

setseed

Simulations are dependent on the starting point of the (pseudo) random number generator. To avoid differences in power for different runs a set.seed() is issued if setseed=TRUE, the default.

Value

• Returns the value of the (empirical) power if argument details=FALSE. Returns a named vector if argument details=TRUE. p(BE) is the power, p(BE-sABEc) is the power of the scaled ABE criterion alone and p(BE-pe) is the power of the criterion 'point estimat within acceptance range' alone. p(BE-ABE) is the power of the conventional ABE test given for comparative purposes.

Warning

In case of the design 2x2x3 heteroscedasticity (CVwT not equal to CVwR) may lead to poor agreement of the power values compared to those calculated via the 'classical' way of subject data sims if the design is unbalanced in respect to the number of subjects in the sequence groups. The function therefore issues a warning for that cases.

Details

The linearized scaled ABE criterion is calculated according to the SAS code given in the FDA progesterone guidance. The simulations are done via the distributional properties of the statistical quantities necessary for deciding BE based on scaled ABE criterion. Details can be found in a document "Implementation_scaledABE_simsVx.yy.pdf" located in the doc subdirectory of the package.

References

FDA "Draft Guidance on Progesterone" Recommended Apr 2010; Revised Feb 2011 http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM209294.pdf 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 Tothfalusi L., Endrenyi L. and A. Garcia Arieta "Evaluation of Bioequivalence for Highly Variable Drugs with Scaled Average Bioequivalence" Clin. Pharmacokin. 48/11, 725-743 (2009)

See Also

sampleN.RSABE, power.scABEL

Examples

# using all the defaults:
# design="2x3x3" -> partial replicate
# ABE limits, PE constraint 0.8-1.25
# true ratio =0.95, 1E+5 simulations
power.RSABE(CV=0.4, n=24)
# should give
# [1] 0.80864
#
# to explore the simulation error due to the state of the
# random number generator
power.RSABE(CV=0.4, n=24, setseed=FALSE)
# will give something like
# [1] 0.8081