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. Typical values are 0.8 or 0.9.

theta0

'True' or assumed bioequivalence ratio. Defaults to 0.90 according to the two Laszlo's if not given explicitly.

theta1

Conventional lower ABE limit to be applied in the mixed procedure if CVsWR <= CVswitch. Also Lower limit for the point estimate constraint. Defaults to 0.8 if not given explicitly.

theta2

Conventional upper ABE limit to be applied in the mixed procedure if CVsWR <= CVswitch. Also upper limit for the point estimate constraint. Defaults to 1.25 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].

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 `design="FDA"`

.
Also the 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 setting
r_const=0.76 to avoid the discontinuity at CV=30% and be more stringent.

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. After reworking the start n in version 1.1-05 seldom needed.

imax

Maximum number of steps in sample size search. Defaults to 100.

print

If `TRUE`

(default) the function prints its results.
If `FALSE`

only the result data.frame will be returned.

details

If set to `TRUE`

, the default, the steps during sample size search are shown.

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(123456)`

is issued if `setseed=TRUE`

, the default.

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.

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 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 Howe-EMA in Munoz et al. and is the standard behavior now if
`regulator="EMA"`

is choosen.

FDA *Draft Guidance on Progesterone*
Recommended Apr 2010. Revised Feb 2011.
download

T<U+00F3>thfalusi, L, Endr<U+00E9>nyi, L.
*Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs*
J Pharm Pharmaceut Sci. 2011;15(1):73--84.
free download

T<U+00F3>thfalusi L, Endr<U+00E9>nyi L, Garc<U+00ED>a Arieta A.
*Evaluation of Bioequivalence for Highly Variable Drugs with Scaled Average
Bioequivalence*
Clin Pharmacokin. 2009;48(11):725--43. 10.2165/11318040-000000000-00000

Mu<U+00F1>oz J, Alcaide D, Oca<U+00F1>a J.
*Consumer<U+2019>s risk in the EMA and FDA regulatory approaches for bioequivalence
in highly variable drugs*
Stat Med. 2015;35(12):1933--43. 10.1002/sim.6834

# NOT RUN { # 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 sims sampleN.RSABE(CV=0.3) # should result in a sample size n=45, power=0.80344 # }