The optimization method minimize requires an initial
design for optimization.
The function get_initial_design provides an initial guess based on a
fixed design that fulfills constraints on type I error rate and power.
Note that a situation-specific initial design may be much more efficient.
Usage
get_initial_design(
theta,
alpha,
beta,
type = c("two-stage", "group-sequential", "one-stage"),
dist = Normal(),
order = 7L,
...
)
Arguments
theta
the alternative effect size in the normal case, the
rate difference under the alternative in the binomial case
alpha
maximal type I error rate
beta
maximale type II error rate
type
is a two-stage, group-sequential, or one-stage design requried?
dist
distribution of the test statistic
order
desired integration order
...
further optional arguments
Details
The distribution of the test statistic is specified by dist.
The default assumes a two-armed z-test.