PowerTOST (version 1.5-6)

sampleN.scABEL.sdsims: Sample size estimation for BE decision via scaled (expanded) BE acceptance limits

Description

These functions performs the sample size estimation via power calculations of the BE decision via scaled (expanded) BE acceptance limits, based on subject data simulations.
This function has an alias sampleN.scABEL.sds().

Usage

sampleN.scABEL.sdsims(alpha = 0.05, targetpower = 0.8, theta0, theta1,
                      theta2, CV, design = c("2x3x3", "2x2x4", "2x2x3"),
                      regulator, nsims = 1e5, nstart, imax = 100,
                      print = TRUE, details = TRUE,
                      setseed = TRUE, progress)

Value

Returns a data.frame with the input settings 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.

Arguments

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 T/R ratio.
Defaults to 0.90 according to the two Lászlós 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

Intra-subject coefficient(s) of variation as ratio (not percent).

• If given as a scalar (length(CV) == 1) the same CV of Test and Reference is assumed (homoscedasticity, CV_wT = CV_wR).

• If given as a vector (length(CV) == 2), i.e., assuming heteroscedasticity, the CV of the Test must be given in CV[1] and the one of the Reference in the CV[2].

design

Design of the study to be planned.
"2x3x3" is the partial replicate design.
"2x2x4" is a full replicate design with 2 sequences and 4 periods.
"2x2x3" is a full replicate design with 2 sequences and 3 periods.
Defaults to "2x3x3". Details are given the section about Designs.

regulator

Regulatory settings for the widening of the BE acceptance limits.
May be given as "EMA", "GCC", or as an object of class 'regSet' (see reg_const).
Defaults to regulator = "EMA" if missing.
This argument may be given also in lower case if given as character.
If given as object of class 'regSet' the component est_method must not be "ISC".

nsims

Number of simulations to be performed to obtain the (empirical) power. The default value 100,000 = 1e+5 is usually sufficient. Consider to rise this value if theta0 <=0.85 or >=1.20. But see the warning section.

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 rarely 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 (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.

progress

Should a progressbar be shown? Defaults to TRUE if missing and nsims >5E5.

Author

H. Schütz

Designs

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

Warning

The sample size estimation for very extreme theta0 (<0.83 or >1.21) may be very time consuming and will eventually also fail since the start values chosen are not really reasonable in that ranges.
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 get caught in the simulation error trap.

Details

The methods rely on the analysis of log-transformed data, i.e., assume a log-normal distribution on the original scale.

The expanded BE acceptance limits will be calculated by the formula
[L, U] = exp(± r_const * sWR)
with r_const the regulatory constant and sWR the standard deviation of the within subjects variability of the Reference. r_const = 0.76 (~log(1.25)/0.29356) is used in case of regulator = "EMA". If the CVwR is < CVswitch=0.3 the conventional ABE limits apply (mixed procedure).
In case of regulator="EMA" a cap is placed on the widened limits if CV_wR > 0.50, i.e., the widened limits are held at value calculated for CV_wR = 0.50.
In case of regulator="GCC" fixed wider limits of 0.7500 -- 1.3333 for CV_wR > 0.30 are applied and the conventional limits otherwise.

The simulations are done by simulating subject data (all effects fixed except the residuals) and evaluating these data via ANOVA of all data to get the point estimate of T vs. R along with its 90% CI and an ANOVA of the data under R(eference) only to get an estimate of s²_wR.

The estimated sample size gives always the total number of subjects (not subject/sequence -- like in some other software packages).

References

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

See Also

power.scABEL.sdsims, sampleN.scABEL, reg_const

Examples

# using the defaults:
# partial replicate design, targetpower=80%,
# true assumed ratio = 0.90, 1E+5 simulated studies
# ABE limits, PE constraint 0.8 - 1.25
# EMA regulatory settings
# Heteroscedasticity (CVwT 0.4, CVwR 0.3)
# compare results and run times
# \donttest{
CV           <- c(0.4, 0.3)
expl         <- data.frame(method = c("subject simulations", "\'key\' statistics"),
                           n = NA, power = NA, seconds = NA)
start        <- proc.time()[[3]]
expl[1, 2:3] <- sampleN.scABEL.sdsims(CV = CV, print = FALSE,
                                      details = FALSE)[8:9]
expl[1, 4]   <- proc.time()[[3]] - start
start        <- proc.time()[[3]]
expl[2, 2:3] <- sampleN.scABEL(CV = CV, print = FALSE,
                               details = FALSE)[8:9]
expl[2, 4]   <- proc.time()[[3]] - start
print(expl, row.names = FALSE)# }
# should result in a sample size n=69, power=0.80198 for
# the subject simulations and n=66, power=0.80775 for the
# 'key' statistics