pa.ABE: Power analysis for average bioequivalence (ABE)

Description

An analysis tool for exploration/visualization of the impact of expected values (CV, GMR, reduced sample size due to drop-outs) on power of BE decision via ABE if these values deviate from the ones assumed in planning the sample size of the study.

Usage

pa.ABE(CV, theta0 = 0.95, targetpower = 0.8, minpower = 0.7, design = "2x2", ...)
## S3 method for class 'pwrA':
print(x, digits=4, plotit=TRUE, \dots)
## S3 method for class 'pwrA':
plot(x, pct=TRUE, cols=c("blue", "red"), ...)

Arguments

Coefficient of variation as ratio. In case of cross-over studies this is the within-subject CV, in case of a parallel-group design the CV of the total variability.

theta0

'True' or assumed bioequivalence ratio. Often named GMR. Must be given as ratio.

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.="" note="" that="" 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. See known.designs() for designs covered in this package.

...

More arguments to pass to power.TOST(). F. i. alpha, theta1, theta2 or robust if other values then the defaults for these arguments are needed. See man page of power.TOST().

Object of class 'pwrA'.

digits

Digits for rounding power in printing. The '...' argument is currently ignored in print().

plotit

If set to TRUE, the default, the print method calls plot(x) if R is running interactively.

pct

If set to TRUE (the default) scales CV and power in percent in plot(). Else they will be given as ratios, the usual standard in PowerTOST.

cols

Colors for the plots. cols[1] gives the color for plotting points with power>targetpower. From targetpower toward minpower the color changes gradually to cols[2].

Value

Returns a list with class "pwrA" with the components
planA data.frame with the result of the sample size estimation. See output of sampleN.TOST().
paCVA data.frame with value pairs CV, pwr for impact of deviations from CV.
paGMRA data.frame with value pairs theta0, pwr for impact of deviations from theta0 (GMR).
paNA data.frame with value pairs N, pwr for impact of deviations from planned N (drop-outs).
methodMethod of BE decision. Here fix = "ABE".
minpowerMinimum acceptable power.
The class 'pwrA' has the S3 methods print() and plot(). See pa.scABE for usage.

Details

Power calculations are done via power.TOST() and calculations of CV and theta0 which gave a power=minpower are derived via R base 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=13353.

Examples

Run this code

# using the defaults
# design="2x2", targetpower=0.8, minpower=0.7, theta0/GMR=0.95
# BE acceptance range from defaults of sampleN.TOST() 0.8 ... 1.25
# print & plot implicit
pa.ABE(CV=0.2)
# print & plot
res <- pa.ABE(CV=0.2)
print(res, plotit=FALSE) # print only
plot(res)

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