Nothing

```
`power.equivalence.md` <-
function(alpha,logscale,ltheta1,ltheta2,ldiff,sigma,n,nu){
# power of the two one-sided tests procedure in bioquivalence on either the regular or the logarithmic scale
# Regular scale:
# K.F. Phillips
# Power of the Two One-Sided Tests Procedure in Bioquivalence
# Journal of Pharmacokinetics and Biopharmaceutics, Vol. 18, No. 2, 1990
# Logarithmic scale:
# E. Diletti, D. Hauschke, V.W. Steinijans
# Sample size determination of bioequivalence assessment by means of confidence intervals
# International Journal of Clinical Pharmacology, Therapy and Toxicology
# Vol. 29, No, 1 - 1991, p1-8
# parameters on the ordinary scale are:
# alpha: alpha level for the 2 t-tests (usually 0.05). Confidence interval for full test is at level 1 - 2 alpha
# logscale: FALSE=use regular scale; TRUE=use logarithmic scale
# ltheta1: lower limit of equivalence interval
# ltheta2: upper limit of equivalence interval
# ldiff: true difference or ratio (log scale) in treatment means
# sigma: sqrt(error variance); ie root MSE from ANOVA (eg, 2 for 2-period crossover)
# n: number of subjects per treatment
# nu: degrees of freedom for sigma
theta1 = ltheta1
theta2 = ltheta2
diff = ldiff
if (logscale){
theta1 = log(ltheta1)
theta2 = log(ltheta2)
diff = log(ldiff)}
power_t <- qt(1.0-alpha,nu,lower.tail=TRUE, log.p = FALSE)
power_l <- (theta2 - theta1)/(2.0*power_t*sqrt(2.0/n)) -.000001
# At sigma=.15, diff=1.1275, n=40, nu=38 the power was underestimated by about 3%
# possibly due to a problem in function integrate.
# Decreasing the upper limit very slightly (.000001) seems to fix the problem
power<-integrate(power.density.equivalence.md, lower=0,upper=power_l,alpha=alpha,theta1= theta1,theta2= theta2,diff=diff,sigma=sigma,n=n,nu=nu,subdivisions=10000,
rel.tol = .Machine$double.eps^0.25, abs.tol = .Machine$double.eps^0.25, stop.on.error = TRUE, keep.xy = FALSE, aux = NULL)[[1]]
return(power)}
```

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