PowerTOST (version 1.3-5)

pa.scABE: Power analysis for scaled average bioequivalence (scABE)

Description

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.

Usage

pa.scABE(CV, theta0=0.9, targetpower=0.8, minpower=0.7, 
         design=c("2x3x3", "2x2x4", "2x2x3"), 
         regulator=c("EMA", "ANVISA", "FDA"), ...)

Arguments

CV

Coefficient of variation of the intra-subject vriability as ratio. Here only the case CVwT=CVwR is implemented, i.e. CV has to be a scalar.

theta0

'True' or assumed bioequivalence ratio. Often named GMR. Must be given as ratio. Defaults to 0.9 here since HVD have a greater scatter in point estimator of T/R.

targetpower

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.="" targetpower="" <="" 0.5="" doesn't="" make="" many="" sense.="" if="" package="" run="" under="" r="" version="" 3.1.0="" targetpower has to be >= 0.5.

minpower

Minimum acceptable power to have if deviating from assumptions for sample size plan. Has to be < as targetpower. Defaults to 0.7. minpower or targetpower

design

Character string describing the study design. Defaults to 2x3x3, the partial replicate design (TRR/RTR/RRT).

regulator

Character string describing the scaled ABE method recommended by the regulatory bodies EMA, ANVISA 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

Value

• Returns a list with class 'pwrA' with the components
• plan
  A data.frame with the result of the sample size estimation. See output of sampleN.scABEL() or sampleN.RSABE()
• .
• paCV
  A data.frame with value pairs CV, pwr for impact of deviations from CV.
• paGMR
  A data.frame with value pairs theta0, pwr for impact of deviations from theta0 (GMR).
• paN
  A data.frame with value pairs N, pwr for impact of deviations from planned N (drop-outs).
• method
  Method of BE decision. Here fix = "scABE".
• regulator
  "EMA" or "FDA".
• minpower
  Minimum acceptable power from the call of the function.
• The class 'pwrA' has the S3 methods print() and plot(). See pa.ABE for usage.

Details

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. It should be kept in mind 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's upto you to decide on reasonable combinations and analyze the power of them.

References

See http://forum.bebac.at/mix_entry.php?id=13376.

See Also

power.scABEL, power.RSABE, print.pwrA, plot.pwrA, pa.ABE

Examples

# using the defaults:
# design="2x3x3", targetpower=0.8, minpower=0.7, theta0/GMR=0.90
# BE acceptance range from defaults of sampleN.scABEL() 0.8 ... 1.25
# 1E5 sims in power.scABEL()
# not run due to timing policy of CRAN, may run some ten seconds
# implicit print & plot
pa.scABE(CV=0.4)