Usage
exppower.TOST(alpha = 0.05, logscale=TRUE, theta0, theta1, theta2,
CV, dfCV, n, design = "2x2", robust=FALSE,
method=c("exact", "approx"))Arguments
alpha
Level of significance. Commonly set to 0.05.
logscale
Should the data used on log-transformed or on original scale? TRUE or FALSE.
Defaults to TRUE.
theta0
'True' or assumed bioequivalence ratio or difference.
Typically set to 0.95 (default if missing) if logscale=TRUE.
Defaults to 0.05 if logscale=FALSE.
theta1
Lower bioequivalence limit as ratio if logscale=TRUE or as difference.
Can be missing. Defaults then to 0.8 if logscale=TRUE or to -0.2 if
logscale=FALSE.
theta2
Upper bioequivalence limit as ratio if logscale=TRUE or as difference.
If not given theta2 will be calculated as 1/theta1 if logscale=TRUE,
else as -theta1.
CV
Coefficient of variation as ratio.
dfCV
Degrees of freedom for the CV (error/residual degree of freedom).
dfCV=Inf is allowed and will result give the same result as
power.TOST(...).
n
Number of subjects under study.
Is total number if given as scalar, else number of subjects in the (sequence)
groups. In the latter case the length of n vector has to be equal to the
number of (sequence) groups.
design
Character string describing the study design.
See known.designs() for designs covered in this package.
robust
Defaults to FALSE.
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 known.designs()$df2 for designs covered in this
method
Defaults to code{method="exact"}.
In that case the expected power will be calculated as expected value of the
power with respect to the (prior) distribution of sigma^2 (inverse gamma
distribution).
Set to method="approx" will calculate the