power.density.equivalence.md {MBESS} | R Documentation |
A function to calculate density for the power of the two one-sided tests prodedure (TOST). (See package equivalence
, function tost
.)
power.density.equivalence.md(power_sigma, alpha = alpha, theta1 = theta1, theta2 = theta2, diff = diff, sigma = sigma, n = n, nu = nu)
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 |
power_density |
{density at diff for power of TOST (probability that (1-2*alpha
) confidence interval will lie within (theta1
,theta2
), given sigma
, n
, and nu
)}
Kem Phillips; kemphillips@comcast.net
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.
power.equivalence.md.plot
, power.density.equivalence.md
, tost
# 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.