An analysis tool for exploration/visualization of the impact of expected values (CV, theta0, reduced sample size due to drop-outs) on power of BE decision via scABE (for highly variable drugs) if these values deviate from the ones assumed in planning the sample size of the study.
pa.scABE(CV, theta0 = 0.9, targetpower = 0.8, minpower = 0.7,
design = c("2x3x3", "2x2x4", "2x2x3"),
regulator = c("EMA", "HC", "FDA"), ...)Coefficient of variation of the intra-subject variability as ratio. Here only the case CVwT=CVwR is implemented, i.e. CV has to be a scalar.
‘True’ or assumed T/R ratio. Often named GMR. Must be given as ratio. Defaults to 0.9 here since HVD have a greater scatter in point estimates of T/R.
Power to achieve at least in sample size estimation. Must be >0 and <1. Typical values are 0.8 or 0.9. Defaults to 0.8. Note that targetpower < 0.5 doesn<U+2019>t make much sense.
Minimum acceptable power to have if deviating from assumptions for sample size plan.
Has to lower than targetpower. Defaults to 0.7.
minpower < 0.5 doesn<U+2019>t make much sense.
Character string describing the study design.
Defaults to 2x3x3, the partial replicate design (TRR|RTR|RRT).
Character string describing the scaled ABE method recommended by the regulatory
bodies EMA, HC, or FDA.
Defaults to EMA, method of scaled (widened) bioequivalence limits.
More arguments to pass to power.scABEL() or power.RSABE().
F.i. alpha, theta1, theta2 or nsims if other values
then the defaults for these arguments are needed.
See man pages of power.scABEL() or power.RSABE().
Returns a list with class 'pwrA' with the components
A data.frame with the result of the sample size estimation.
See output of sampleN.scABEL() or sampleN.RSABE().
A data.frame with value pairs CV, pwr for impact of deviations from CV.
A data.frame with value pairs theta0, pwr for impact of deviations from theta0 (GMR).
A data.frame with value pairs N, pwr for impact of deviations from planned N (dropouts).
Method of BE decision. Here fix = "scABE".
"EMA", "HC", or "FDA".
Minimum acceptable power from the call of the function.
The class 'pwrA' has the S3 methods print() and plot(). See pa.ABE for usage.
Power calculations are done via power.scABEL() or power.RSABE() and
calculations of CV and theta0 which result in minpower are derived via uniroot().
While one of the parameters (CV, GMR, N) is varied, the respective two others are
kept constant. The tool shows the relative impact of single parameters on power.
The tool takes a minimum of 12 subjects as demanded in most BE guidances into account.
However, it should be kept in mind that
the FDA requires at least 24 subjects enrolled in studies intended for reference-scaling;
the EMA requires at least 12 eligible subjects in the sequence RTR of the TRT|RTR-design.
You should be aware that this is not a substitute for the “Sensitivity Analysis” recommended in ICH-E9. In a real study a combination of all effects occurs simultaneously. It is up to you to decide on reasonable combinations and analyze their respective power.
power.scABEL, power.RSABE, known.designs,
print.pwrA, plot.pwrA, pa.ABE, pa.NTIDFDA
# NOT RUN {
# using the defaults:
# design="2x3x3", targetpower=0.8, minpower=0.7, theta0/GMR=0.90
# widened BE margins from defaults of sampleN.scABEL() 0.7462 ... 1.3402
# 1E5 sims in power.scABEL()
# not run due to timing policy of CRAN, may run some ten seconds
# }
# NOT RUN {
# implicit print & plot
pa.scABE(CV=0.4)
# }
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