PowerTOST (version 1.3-5)

power.TOST.sim: Power of the TOST procedure obtained via simulations

Description

Power is calculated by simulations of studies (PE via it's normal distribution, MSE via it's associated chi-squared distribution) and application of the two one-sided t-tests. Power is obtained via ratio of studies found BE to # of simulated studies.

Usage

power.TOST.sim(alpha = 0.05, logscale = TRUE, theta1, theta2, theta0, CV, n, 
               design = "2x2", robust = FALSE, setseed = TRUE, nsims = 1e+05)

Arguments

alpha

Type I error probability, significance level. By convention mostly set to 0.05.

logscale

Should the data used on log-transformed or on original scale? TRUE or FALSE. Defaults to TRUE.

theta1

Lower bioequivalence limit. In case of logscale=TRUE it is given as ratio, otherwise as diff. to 1. Defaults to 0.8 if logscale=TRUE or to -0.2 if logscale=FALSE.

theta2

Upper bioequivalence limit. If not given theta2 will be calculated as 1/theta1 if logscale=TRUE or as -theta1 if logscale=FALSE.

theta0

'True' or assumed bioequivalence ratio. In case of logscale=TRUE it must be given as ratio, otherwise as difference to 1. See examples. Defaults to 0.95 if logscale=TRUE or to 0.05 if logscale=FALSE

CV

Coefficient of variation as ratio. In case of cross-over studies this is the within-subject CV, in case of a parallel-group design the CV of the total variability.

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. 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

setseed

Simulations are dependent on the starting point of the (pseudo) random number generator. To avoid differences in power for different runs a set.seed(1234567) is issued if setseed=TRUE, the default. Set this argument to

nsims

Number of studies to simulate. Defaulsts to 1E5 = 100 000.

Value

• Value of power according to the input arguments.

See Also

power.TOST,

Examples

# using the default design 2x2, BE range 0.8 ... 1.25, logscale, theta0=0.95
power.TOST.sim(alpha=0.05, CV=0.3, n=12)
# should give 0.15054, with nsims=1E6 it will be 0.148533
# exact analytical is
power.TOST(alpha=0.05, CV=0.3, n=12)
# should give 0.1484695

# very unusual alpha setting
power.TOST.sim(alpha=0.9, CV=0.3, n=12)
# should give the same (within certain precision) as
power.TOST(alpha=0.95, CV=0.3, n=12)
# or also within certain precision equal to  
power.TOST(alpha=0.95, CV=0.3, n=12, method="mvt")
# SAS Proc Power gives here the incorrect value 0.60525