power.tsd.fC {Power2Stage} | R Documentation |
Power calculation of adaptive 2-stage BE studies (2x2 crossover) with a futility criterion for the point estimate of T/R or its 90% CI
Description
This function calculates the ‘empiric’ power of 2-stage BE studies according to Potvin et al. ‘method B/C’ via simulations. The Potvin methods are modified to include a futility criterion for the point estimate or for its 90%CI and to allow the sample size estimation step to be done with the point estimate (PE) and MSE of stage 1.
Usage
power.tsd.fC(method = c("B", "C", "B0"), alpha0 = 0.05, alpha = c(0.0294, 0.0294),
n1, CV, GMR, targetpower = 0.8, pmethod = c("nct", "exact", "shifted"),
usePE = FALSE, powerstep = TRUE, min.n2=0, max.n=Inf,
fCrit=c("CI", "PE"), fClower, fCupper, theta0, theta1, theta2,
npct = c(0.05, 0.5, 0.95), nsims, setseed = TRUE, details = FALSE)
Arguments
method |
Decision schemes according to Potvin et.al. (defaults to |
alpha0 |
Alpha value for the first step(s) in Potvin |
alpha |
Vector (two elements) of the nominal alphas for the two stages. Defaults to
Pocock’s setting |
n1 |
Sample size of stage 1. For Xu’s methods the recommended sample size should be at least 18 (if CV 10–30%) or 48 (if CV 30–55%). |
CV |
Coefficient of variation of the intra-subject variability (use e.g., 0.3 for 30%). |
GMR |
Ratio T/R to be used in decision scheme (power calculations in stage 1 and sample size estimation for stage 2). |
targetpower |
Power threshold in the power monitoring steps and power to achieve in the sample size estimation step. |
pmethod |
Power calculation method, also to be used in the sample size estimation for
stage 2. |
usePE |
If |
powerstep |
If |
min.n2 |
Minimum sample size of stage 2. Defaults to zero. |
max.n |
If |
fCrit |
Futility criterion. |
fClower |
Lower futility limit for the |
fCupper |
Upper futility limit for the |
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 |
Details
The calculations follow in principle the simulations as described in Potvin
et al.
The underlying subject data are assumed to be evaluated after log-transformation.
But instead of simulating subject data, the statistics pe1, mse1 and pe2, SS2 are
simulated via their associated distributions (normal and
χ2 distributions).
Value
Returns an object of class "pwrtsd"
with all the input arguments and results
as components.
The class "pwrtsd"
has an S3 print method.
The results are in the components:
pBE |
Fraction of studies found BE. |
pBE_s1 |
Fraction of studies found BE in stage 1. |
pct_s2 |
Percentage of studies continuing to stage 2. |
nmean |
Mean of n(total), aka average total sample size (ASN). |
nrange |
Range (min, max) of n(total). |
nperc |
Percentiles of the distribution of n(total). |
ntable |
Object of class |
Author(s)
D. Labes
References
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
Montague TH, Potvin D, DiLiberti CE, Hauck WW, Parr AF, Schuirmann DJ. Additional results for ‘Sequential design approaches for bioequivalence studies with crossover designs’.
Pharm Stat. 2011; 11(1):8–13. doi: 10.1002/pst.483
Fuglsang A. Sequential Bioequivalence Trial Designs with Increased Power and
Controlled Type I Error Rates.
AAPS J. 2013; 15(3):659–61. doi: 10.1208/s12248-013-9475-5
Schütz H. Two-stage designs in bioequivalence trials.
Eur J Clin Pharmacol. 2015; 71(3):271–81. doi: 10.1007/s00228-015-1806-2
Kieser M, Rauch G. Two-stage designs for cross-over bioequivalence trials.
Stat Med. 2015; 34(16):2403–16. doi: 10.1002/sim.6487
Zheng Ch, Zhao L, Wang J. Modifications of sequential designs in bioequivalence trials.
Pharm Stat. 2015; 14(3):180–8. doi: 10.1002/pst.1672
Xu J, Audet C, DiLiberti CE, Hauck WW, Montague TH, Parr TH, Potvin D, Schuirmann DJ.
Optimal adaptive sequential designs for crossover bioequivalence studies.
Pharm Stat. 2016;15(1):15–27. doi: 10.1002/pst.1721
See Also
Examples
# using all the defaults
power.tsd.fC(CV=0.25, n1=24)
# run-time ~1 sec
## Not run:
# as above but storing the results
res <- power.tsd.fC(CV=0.25, n1=24)
# representation of the discrete distribution of n(total)
# via plot method of object with class "table" which creates a
# 'needle' plot
plot(res$ntable/sum(res$ntable), ylab="Density",
xlab=expression("n"[total]), las=1,
main=expression("Distribution of n"[total]))
## End(Not run)