View source: R/power_RSABE2L_sdsims.R
power.RSABE2L.sdsims  R Documentation 
These function performs the power calculation of the BE decision via
the reference scaled ABE based on subject data simulations.
Implemented are the methods ABEL, Hyslop and ‘exact’ as described in the
references.
The estimation method of the key statistics needed to perform the RSABE decision
is the usual ANOVA.
power.RSABE2L.sdsims(alpha = 0.05, theta1, theta2, theta0, CV, n, design = c("2x3x3", "2x2x4", "2x2x3"), design_dta = NULL, SABE_test = "exact", regulator, nsims = 1e+05, details = FALSE, setseed = TRUE, progress)
alpha 
Type I error probability, significance level. Conventionally mostly set to 0.05. 
theta1 
Conventional lower ABE (Average Bioequivalence) 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. 
design_dta 
Alternatively to using the arguments 
SABE_test 
This argument specifies the test method to be used for the reference scaled
ABE decision. 
regulator 
Regulatory settings for the widening of the BE acceptance limits. 
nsims 
Number of simulations to be performed to obtain the empirical power.
Defaults to 100,000 = 1e+05. 
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 
progress 
Should a progressbar be shown? Defaults to 
The methods rely on the analysis of logtransformed data, i.e., assumes a
lognormal distribution on the original scale.
The data.frame with columns subject, sequence, period
and tmt
necessary for evalution of simulated subject data is constructed internally from
the arguments design
and n
or may be given user defined via the argument
design_dta
. The last option is usefull if missing data have to be considered
or if designs have to be evaluated which are not in the list of argument
design
.
The estimation method for obtaining the statistics necessary to perform the
reference scaled ABE decision is the usual ANOVA with effects treatment, period,
sequence and subject within sequence for the evaluation of all data and period,
sequence and subject within sequence for the evaluation of the Reference formulation
data only.
The SABE tests implemented are:
"exact"  ‘exact’ based method of the two Laszlós (see references, called there ‘ncTOST’) 
"ABEL"  Average bioequivalence with expanding limits 
"hyslop"  BE decision via the linearized RSABE criterion and its upper 95% CI 
"fda"  Hyslop with an additional bias correction term as implemented in the SAS code of the 
FDA’s Guidance on Progesterone. 
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(BERSABE)
is the power of using the reference
scaled ABE alone, and p(BEpe)
is the power of the criterion
‘point estimate 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 
The function is relatively slow. The runtime for 1 Mio. simulations
is between ~ 1 up to 6 minutes for n=12 or n=120 and 1 Mio. sim’s
(see the call under examples) on a machine with an Intel core i7 processor.
Thus be patient and go for a cup of coffee if you use this function with higher
sample sizes and aim for estimating the type 1 error!
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. An Exact Procedure for the Evaluation of ReferenceScaled Average Bioequivalence. AAPS J. 2016;18(2):476–89. doi: 10.1208/s1224801698736.
Tóthfalusi L, Endrényi L. Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk. Stat Med. 2017;36(27):4378–4390. doi: 10.1002/sim.7440
power.RSABE, reg_const
# Not run due to timing policy of CRAN # pure EMA settings without mixed procedure, cap on widening and PE constraint # as in the reference from 2017 reg < reg_const("EMA") reg$CVswitch < 0 reg$CVcap < Inf reg$pe_constr < FALSE reg$name < "EMA pure" power.RSABE2L.sds(CV = 0.4, n = 12, theta0 = exp(0.05), design = "2x2x4", regulator = reg, nsims = 50000) # should give: # [1] 0.46504 (compared to 47.1% in the 2017 reference)
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