Estimates the power of hypothesis testing in equivalence trials using the method described by Mielke et al. This approach accounts for multiple endpoints, correlation structures, and multiplicity adjustments.
power_Mielke(
N,
m,
k,
R,
sigma,
true.diff,
equi.tol = log(1.25),
design,
alpha = 0.05,
adjust = "no",
nsim = 10000
)A numeric value representing the estimated power based on the simulations.
Integer specifying the number of subjects per sequence.
Integer specifying the number of endpoints.
Integer specifying the number of endpoints that must meet equivalence to consider the test successful.
Matrix specifying the correlation structure between endpoints.
This should be an m x m matrix, e.g., generated using variance.const.corr().
Numeric specifying the standard deviation of endpoints.
Can be a vector of length m (one per endpoint) or a single value.
In a 2x2 crossover design, this represents within-subject variance.
In a parallel-group design, it represents the treatment group standard deviation.
Numeric specifying the assumed true difference between test and reference.
Can be a vector of length m or a single value.
Numeric specifying the equivalence margins, with the interval defined as
(-equi.tol, +equi.tol). Default is log(1.25).
Character specifying the study design.
Options are "22co" for a 2x2 crossover design or "parallel" for a parallel-group design.
Numeric specifying the significance level. Default is 0.05.
Character specifying the method for multiplicity adjustment.
Options include "no" for no adjustment, "bon" for Bonferroni correction,
and "k" for k-adjustment.
Integer specifying the number of simulations to perform. Default is 10,000.