PowerTOST (version 1.2-09)

exppower.noninf: 'Expected' power of non-inferiority test

Description

Calculates the 'expected' power according to Julious for a variety of study designs used in bioequivalence studies.

Usage

exppower.noninf(alpha = 0.025, logscale=TRUE, theta0, margin,  
                CV, dfCV, n, design = "2x2", robust=FALSE)

Arguments

alpha

Type I error probability, significance level. Defaults here to 0.025.

logscale

Should the data used on log-transformed or on original scale? TRUE or FALSE. Defaults to TRUE.

theta0

'True' or assumed bioequivalence ratio or difference. Typically set to 0.95 (default if missing) if logscale=TRUE. Defaults to -0.05 if logscale=FALSE.

margin

Non-inferiority margin. In case of logscale=TRUE it must be given as ratio, otherwise as diff. Defaults to 0.8 if logscale=TRUE or to -0.2 if logscale=FALSE.

CV

Coefficient of variation as ratio.

dfCV

Degrees of freedom for the CV (error/residual degree of freedom).

n

Number of subjects under study. Is total number if given as scalar, else number of subjects in the (sequence) groups. In the latter case the length of n vector has to be equal to the number of (sequence) groups.

design

Character string describing the study design. See known.designs() for designs covered in this package.

robust

Defaults to FALSE. Set to TRUE will use the degrees of freedom according to the 'robust' evaluation (aka Senn's basic estimator). These df are calculated as n-seq. See known.designs()$df2 for designs covered in this

Value

• Value of expected power according to the input.

Details

This function calculates the so-called 'expected' power based on formulas according to S.A. Julious. These take into account that usually the CV is not known but estimated from a previous study / studies with an uncertainty. See references.

References

S.A. Julious "Sample sizes for Clinical Trials" CRC Press, Chapman & Hall 2010

See Also

expsampleN.noninf, power.noninf, power.TOST

Examples

# expected power for non-inferiority test of a 2x2 crossover
# CV 30\% known from a pilot study with 12 subjects (-> dfCV=10)
# using all the defaults for other parameters
# should give: [1] 0.6751358
exppower.noninf(CV=0.3, dfCV=10, n=40)

# Compare this to the usual power (CV known, "carved in stone")
# should give: [1] 0.7228685
power.noninf(CV=0.3, n=40)