View source: R/samplesize_RSABE.R
sampleN.RSABE  R Documentation 
This function performs the sample size estimation for the BE decision via linearized scaled ABE criterion based on simulations.
sampleN.RSABE(alpha = 0.05, targetpower = 0.8, theta0, theta1, theta2, CV, design = c("2x3x3", "2x2x4", "2x2x3"), regulator = c("FDA", "EMA"), nsims = 1e+05, nstart, imax=100, print = TRUE, details = TRUE, setseed=TRUE)
alpha 
Type I error probability. Per convention mostly set to 0.05. 
targetpower 
Power to achieve at least. Must be >0 and <1. 
theta0 
‘True’ or assumed T/R ratio. 
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

CV 
Intrasubject coefficient(s) of variation as ratio (not percent).

design 
Design of the study to be planned. 
regulator 
Regulatory body settings for the scaled ABE criterion. 
nsims 
Number of simulations to be performed to obtain the (empirical) power. 
nstart 
Set this to a start for the sample size search if a previous run failed. 
imax 
Maximum number of steps in sample size search. Defaults to 100. 
print 
If 
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 progesterone guidance.
The simulations are done via the distributional properties of the statistical
quantities necessary for deciding BE based on scaled ABE.
For more details see a document Implementation_scaledABE_simsVx.yy.pdf
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.
The estimated sample size gives always the total number of subjects (not subject/sequence – like in some other software packages).
Returns a data.frame with the input and sample size results.
The Sample size
column contains the total sample size.
The nlast
column contains the last n
value. May be useful for restarting.
Although some designs are more ‘popular’ than others, sample size estimations 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 sample size estimation for theta0 >1.2 and <0.85 may be very time consuming
and will eventually also fail since the start values chosen are not really
reasonable in that ranges. This is especially true in the range about CV = 0.3
and regulatory constant according to FDA.
If you really need sample sizes in that range be prepared to restart the sample
size estimation via the argument nstart.
Since the dependence of power from n is very flat in the mentioned region you
may also consider to adapt the number of simulations not to tap in the simulation
error trap.
The sample size estimation is done only for balanced designs since the
break down of the total subject number in case of unbalanced sequence groups
is not unique. Moreover the formulas used are only for balanced designs.
The minimum sample size is n=6, even if the power is higher than the intended
targetpower.
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
power.RSABE
, power.scABEL
# using all the defaults: # design=2x3x3 (partial replicate design), theta0=0.90, # ABE limits, PE constraint 0.8  1.25 # targetpower=80%, alpha=0.05, 1E5 simulations sampleN.RSABE(CV = 0.3) # should result in a sample size n=45, power=0.80344
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