PowerTOST (version 1.1-08)

power.noninf: Power of the one-sided non-inferiority t-test

Description

Function calculates of the power of the one-sided non-inferiority t-test for normal or log-normal distributed data.

Usage

power.noninf(alpha = 0.025, logscale = TRUE, margin, theta0, CV, 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.

margin

Non-inferiority margin. In case of logscale=TRUE it must be given as ratio, otherwise as diff. to 1. Defaults to 0.8 if logscale=TRUE or to -0.2 if logscale=FALSE.

theta0

'True' or assumed bioequivalence ratio or difference. In case of logscale=TRUE it must be given as ratio, otherwise as difference to 1. See examples. Defaults to 0.95 if logscale=TRUE or to -0.05 if logscale=FALSE.

CV

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.

n

Number of subjects under study.

design

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

robust

Defaults to FALSE. With that value the usual degrees of freedom will be used. 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 <

Value

• Value of power according to the input arguments.

Warning

The function does not vectorize if design is a vector. The function vectorize properly if n or CV or theta0 are vectors. Other vector input is not tested yet.

Details

The power is calculated via non-central t-distribution.

References

S.A. Julious "TUTORIAL IN BIOSTATISTICS Sample sizes for clinical trials with Normal data" Statist. Med. 2004; 23: 1921-1986

See Also

known.designs, sampleN.noninf

Examples

# using all the defaults: margin=0.8, theta0=0.95, alpha=0.025
# log-transformed, design="2x2"
# should give: 0.4916748
power.noninf(CV=0.3, n=24)
#
# vectorized answer
# should give: [1] 0.7146648 0.8227931 0.8926036
power.noninf(alpha=0.05, CV=0.3, n=c(30,40,50), design="2x2")