View source: R/power_TOST_sds.R
power.TOST.sds  R Documentation 
The power is obtained via subject data simulations.
Three models are implemented:
gmodel==1 is full FDA model for testing groupbytreatment interaction followed by gmodel==2 or gmodel==3 with data of the biggest group depending on the test of the treatment by group interaction
gmodel==2 is full FDA model but without groupbytreatment interaction
gmodel==3 is model with pooled groups, i.e. without any group term
power.TOST.sds(alpha = 0.05, theta1, theta2, theta0, CV, n, design = c("2x2", "2x2x2", "2x3x3", "2x2x4", "2x2x3"), design_dta = NULL, grps = 2, ngrp = NULL, gmodel = 2, nsims = 1e+05, details = FALSE, setseed = TRUE, progress)
alpha 
Type I error probability, significance level. Conventionally mostly set to 0.05. 
theta1 
Lower BE limit. Defaults to 0.8 if not given explicitely. 
theta2 
Upper BE limit. Defaults to 1.25 if not given explicitely. 
theta0 
‘True’ or assumed T/R ratio. 
CV 
Intrasubject coefficient(s) of variation as ratio (not percent).

n 
Number of subjects under study. 
design 
Design of the study to be planned. 
design_dta 
Alternatively to using the arguments 
grps 
Number of (logistical) groups. Defaults to 2. 
ngrp 
Vector of number of subjects in groups. 
gmodel 
Number describing the model incorporating group effects
Defaults to 
nsims 
Number of simulations to be performed to obtain the empirical power.
Defaults to 100,000 = 1e+05. 
details 
If set to 
setseed 
Simulations are dependent on the starting point of the (pseudo) random number
generator. To avoid differences in power for different runs a 
progress 
Should a progressbar be shown? Defaults to 
The power is calculated via subject data sims.
The evaluation of BE is done via 12*alpha confidence interval using classical ANOVA
for the models with group effects.
The data.frame with columns subject, sequence, period
and tmt
necessary for evaluation of simulated subject data is constructed internally from
the arguments design
and n
or may be given user defined via the argument
design_dta
. The last option is usefull if missing data have to be considered
or if designs have to be evaluated which are not in the list of argument
design
.
This feature is experimental in the sense that the data.frame is not checked
for complying with the assumed structure.
The test of the treatment by group interaction in case of gmodel=1
is hard coded with p.level = 0.1.
If the treatment by group interaction is significant the subsequent BE decision
is done with the data of the largest group. If there are more than one with the same size, one gets a warning that this feature – showing BE in all that groups – isn’t implemented yet.
Only the first of the largest groups is tested for BE.
Returns the value of the (empirical) power
The run time of the function may be relatively long.
Take a cup of coffee and be patient.
D. Labes
Schütz H.
MultiGroup Studies in Bioequivalence. To pool or not to pool?
Presentation at BioBriges 2018, Prague. https://bebac.at/lectures/Prague2018.pdf
# power for gmodel=2, 2x2 crossover, grps=3 with even number of subjects power.TOST.sds(CV=0.2, n=18, grps=3) # gives [1] 0.78404 # without considering groups power.TOST.sds(CV=0.2, n=18, gmodel=3) # gives [1] 0.7887
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