MBESS (version 4.3.0)

power.density.equivalence.md: Density for power of two one-sided tests procedure (TOST) for equivalence

Description

A function to calculate density for the power of the two one-sided tests prodedure (TOST). (See package equivalence, function tost.)

Usage

power.density.equivalence.md(power_sigma, alpha = alpha, theta1 = theta1, 
theta2 = theta2, diff = diff, sigma = sigma, n = n, nu = nu)

Arguments

power_sigma

x-value for integration

alpha

alpha level for the 2 t-tests (usually alpha=0.05). Confidence interval for full test is at level (1-2*\(\alpha\))

theta1

lower limit of equivalence interval on appropriate scale (regular or log)

theta2

upper limit of equivalence interval on appropriate scale

diff

true difference (ratio on log scale) in treatment means on appropriate scale

sigma

sqrt(error variance) as fraction (root MSE from ANOVA, or coefficient of variation)

n

number of subjects per treatment (number of total subjects for crossover design)

nu

degrees of freedom for sigma

Value

power_density

density at diff for power of TOST: the probability that the confidence interval will lie within ['theta1', 'theta2']

References

Diletti, E., Hauschke D. & Steinijans, V.W. (1991). Sample size determination of bioequivalence assessment by means of confidence intervals, International Journal of Clinical Pharmacology, Therapy and Toxicology, 29, No. 1, 1--8.

Phillips, K.F. (1990). Power of the Two One-Sided Tests Procedure in Bioquivalence, Journal of Pharmacokinetics and Biopharmaceutics, 18, No. 2, 139--144.

Schuirmann, D.J. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability, Journal of Pharmacokinetics and Biopharmaceutics, 15. 657--680.

See Also

power.equivalence.md.plot, power.density.equivalence.md

Examples

# This function is called by power.equivalence.md within 
# the integrate function. It is integrated over 
# appropriate limits to compute the power. Use

power.density.equivalence.md(.1, alpha=.05, theta1=-.2, theta2=.2, diff=.05, 
	sigma= .20, n=24, nu=22)

# The usage for the logarithmic scale is the same, except that 
# theta1, theta2, and diff must be on that scale. That is, use log(.8), etc.