Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function calculates the ‘empiric’ power (via simulations) of 2stage BE studies with interim sample size reestimation (i.e., but no BE decision after stage 1). The sample size reestimation can be done blinded or unblinded.
1 2 3 4 
alpha 
Nominal type I error. Has to be adjusted in case of inflation of the Type I Error. 
n1 
Sample size of stage 1. 
GMR 
Ratio T/R to be used in the sample size reestimation. 
CV 
Coefficient of variation of the intrasubject variability
(use e.g., 0.3 for 30%). 
targetpower 
Power to achieve in the sample size estimation step. 
pmethod 
Power calculation method to be used in the sample size reestimation for
stage 2. 
blind 
If 
usePE 
If 
min.n 
If 
max.n 
If 
theta0 
Assumed ratio of geometric means (T/R) for simulations. If missing, defaults
to 
theta1 
Lower bioequivalence limit. Defaults to 0.8. 
theta2 
Upper bioequivalence limit. Defaults to 1.25. 
npct 
Percentiles to be used for the presentation of the distribution of

nsims 
Number of studies to simulate. 
setseed 
Simulations are dependent on the starting point of the (pseudo) random number
generator. To avoid differences in power for different runs a

details 
If set to 
The calculations follow in principle the simulations as described in Potvin
et al.
The underlying subject data are assumed to be evaluated after logtransformation.
But instead of simulating subject data, the statistics pe1, mse1 and pe2, SS2 are
simulated via their associated distributions (normal and
χ^{2} distributions).
Returns an object of class class "pwrtsd"
with all the input arguments and results
as components.
The class class "pwrtsd"
has a S3 print method.
The results are in the components:
pBE 
Fraction of studies found BE. 
pct_s2 
Percentage of studies continuing to stage 2. 
nmean 
Mean of n(total). 
nrange 
Range (min, max) of n(total). 
nperc 
Percentiles of the distribution of n(total). 
ntable 
Object of class 
The computation time is in the magnitude of a few seconds for 100,000 sim’s
on my machine (Intel core i7 2.5 GHz, 12GB RAM) if the noncentral t
approximation is used. Thus be a bit patient if you simulate for the Tpye I Error
'alpha' with 1 Mio sim’s.
Using the crude pmethod="ls"
on the other hand results in a nearly immediate
sample size reestimation.
D. Labes
Golkowski D, Friede T, Kieser M. Blinded sample size reestimation in crossover bioequivalence trials.
Pharm Stat. 2014; 13(3):157–62. doi: 10.1002/pst.1617
Jones B, Kenward MG. Design and Analysis of CrossOver Trials.
Boca Raton: CRC Press; 3^{rd} edition 2014. Chapter 12.
Potvin D, DiLiberti CE, Hauck WW, Parr AF, Schuirmann DJ, Smith RA. Sequential design approaches for bioequivalence studies with crossover designs.
Pharm Stat. 2008; 7(4):245–62. doi: 10.1002/pst.294
power.tsd
for 2stage studies with interim BE decision.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Not run to comply with CRAN policy about examples' runtime;
# minimum number of sim's should be 1E5 for 'power', 1E6 sim's for 'alpha'
## Not run:
power.tsd.ssr(alpha=0.05, n1=10, GMR=1, CV=0.239, targetpower=0.9,
pmethod="ls", blind=TRUE, theta0=1.25)
# should give an alphainflation 0.072359 (run time <5 seconds)
# repeated with noncentral tapproximation
power.tsd.ssr(alpha=0.05, n1=10, GMR=1, CV=0.239, targetpower=0.9,
pmethod="nct", blind=TRUE, theta0=1.25)
# should give an alphainflation 0.069789 (run time ~20 seconds)
#
# adjusted alpha to control the Type I Error, noncentral tapprox.
power.tsd.ssr(alpha=0.03505, n1=10, GMR=1, CV=0.239, targetpower=0.9,
pmethod="nct", blind=TRUE, theta0=1.25)
# should control the TIE with 0.049877
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.