Usage
expsampleN.TOST(alpha = 0.05, targetpower = 0.8, logscale=TRUE,
theta0, theta1, theta2, CV, dfCV, design = "2x2",
robust=FALSE, print = TRUE, details = FALSE, imax=100)Arguments
alpha
Error probability. Typically set to 0.05.
targetpower
Power to achieve at least. Must be >0 and
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.
Maybe missing. Defaults the to 0.95 if logscale=TRUE or 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.
May be given as vector. Then the CV's were pooled as weighted mean with
their df=degrees of freedom as weights.
dfCV
Degrees of freedom for the CV's. Must be a vector of same length as CV.
design
Character string describing the study design.
See known.designs() for designs covered in this package.
robust
Defaults to FALSE. With that value the usual degrees of freedom will be used.
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 <
print
If TRUE (default) the function prints its results.
If FALSE only a data.frame with the results will be returned.
details
If TRUE the design characteristics and the steps during
sample size calculations will be shown.
Defaults to FALSE.
imax
Maximum number of steps in sample size search.
Defaults to 100. Adaption only in very rare cases needed.
Never seen a need for adaption up to now.