power.RatioF: Power for equivalence of the ratio of two means with normality on original scale

Description

Calculates the power of the test of equivalence of the ratio of two means with normality on original scale. This test is based on Fieller's confidence ('fiducial') interval and Sasabuchi's test (again a TOST procedure).

Usage

power.RatioF(alpha = 0.025, theta1 = 0.8, theta2, theta0 = 0.95, CV, CVb, n,   
             design = "2x2", setseed=TRUE)

Arguments

alpha

Type I error probability, aka significance level. Defaults here to 0.025 because this function is intended for studies with clinical endpoints.

theta1

Lower bioequivalence limit. Typically 0.8 (default).

theta2

Upper bioequivalence limit. Typically 1.25. Is set to 1/theta1 if missing.

theta0

True ('null') assumed bioequivalence ratio. Typically set to 0.95 for planning.

Coefficient of variation as ratio. In case of design="parallel" this is the CV of the total variability, in case of design="2x2" the intra-subject CV (CVw in the reference).

CVb

CV of the between-subject variability. Only necessary for design="2x2".

Number of subjects to be planned. n is for both designs implemented the total number of subjects.

design

A character string describing the study design. design="parallel" or design="2x2" allowed for a two-parallel group design or a classical TR/RT crossover design.

setseed

If set to TRUE the dependence of the power from the state of the random number generator is avoided. With setseed = FALSE you may see the dependence from the state of the random number generator.

Value

Value of power according to the input.

Details

The power is calculated exact using the bivariate non-central t-distribution via function pmvt() from the package mvtnorm. Due to the calculation method of the used package mvtnorm - randomized Quasi-Monte-Carlo - these probabilities are dependent from the state of the random number generator within the precision of the power. See argument setseed.

References

Hauschke D., Kieser M., Diletti E. and Burke M. "Sample size determination for proving equivalence based on the ratio of two means for normally distributed data" Stat. Med. 18(1) p93-105 (1999) Hauschke D., Steinijans V. and Pigeot I. "Bioequivalence Studies in Drug Development" Chapter 10., John Wiley & Sons, Chichester (2007)

Examples

Run this code

# power for alpha=0.025, ratio0=0.95, theta1=0.8, theta2=1/theta1=1.25
# within-subject CV=0.2, between-subject CV=0.4 
# 2x2 crossover study, n=24
# using all the defaults:
power.RatioF(CV=0.2, CVb=0.4, n=24)
# gives [1] 0.7315357

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning