power.RSABE  R Documentation 
This function performs the power calculation of the BE decision via linearized scaled ABE criterion by simulations as recommended by the FDA.
power.RSABE(alpha = 0.05, theta1, theta2, theta0, CV, n, design = c("2x3x3", "2x2x4", "2x2x3"), regulator, nsims = 1e+05, details = FALSE, setseed=TRUE)
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

theta2 
Conventional upper ABE limit to be applied in the mixed procedure if

theta0 
‘True’ or assumed T/R ratio. 
CV 
Intrasubject coefficient(s) of variation as ratio (not percent).

n 
Number of subjects under study. 
design 
Design of the study. 
regulator 
Regulatory settings for RSABE. 
nsims 
Number of simulations to be performed to obtain the empirical power.
Defaults to 100,000 = 1e+5. 
details 
If set to 
setseed 
Simulations are dependent on the starting point of the (pseudo) random number
generator. To avoid differences in power for different runs a 
The linearized scaled ABE criterion is calculated according to the SAS code
given in the FDA’s 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.
If a CVcap is defined for the regulator, the BE decision is based on the inclusion
of the CI in the capped widened acceptance limits in case of CVwR > CVcap
. This
resembles method ‘HoweEMA’ in Muñoz et al. and is the standard behavior now if
regulator="EMA"
is choosen.
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(BEsABEc) is the power of the scaled ABE criterion alone
and p(BEpe) is the power of the criterion ‘point estimat within acceptance
range’ alone.
p(BEABE) is the power of the conventional ABE test given for comparative purposes.
Although some designs are more ‘popular’ than others, power calculations are valid for all of the following designs:
"2x2x4"  TRTR  RTRT 
TRRT  RTTR  
TTRR  RRTT  
"2x2x3"  TRT  RTR 
TRR  RTT  
"2x3x3"  TRR  RTR  RRT 
In case of the design "2x2x3"
" heteroscedasticity (i.e., CVwT != CVwR) may
lead to poor agreement of the power values compared to those calculated via the
‘classical’ way of subject data simulations if the design is unbalanced in respect
to the number of subjects in the sequence groups. Therefore, the function
issues a warning for that cases.
D. Labes
Food and Drug Administration, Office of Generic Drugs (OGD). Draft Guidance on Progesterone. Recommended Apr 2010. Revised Feb 2011. download
Tóthfalusi, L, Endrényi, L. Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs. J Pharm Pharmaceut Sci. 2011;15(1):73–84. open access
Tóthfalusi L, Endrényi L, García Arieta A. Evaluation of Bioequivalence for Highly Variable Drugs with Scaled Average Bioequivalence. Clin Pharmacokin. 2009;48(11):725–43. doi: 10.2165/1131804000000000000000
Muñoz J, Alcaide D, Ocaña J. Consumer’s risk in the EMA and FDA regulatory approaches for bioequivalence in highly variable drugs. Stat Med. 2015;35(12):1933–43. doi: 10.1002/sim.6834
sampleN.RSABE
, power.scABEL
# using all the defaults: # design="2x3x3" = partial replicate # ABE limits, PE constraint 0.81.25 # true ratio = 0.90, 1E+5 simulations power.RSABE(CV = 0.4, n = 36) # should give # [1] 0.83634 # # to explore the simulation error due to the state of the # random number generator power.RSABE(CV = 0.4, n = 36, setseed = FALSE) # will give something like # [1] 0.83725 # # explore pure RSABE (without mixed method, without pe constraint) rs < reg_const("FDA") rs$CVswitch < 0 rs$pe_constr < FALSE power.RSABE(CV = 0.4, n = 36, regulator = rs) # should give # [1] 0.84644
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