Description Usage Arguments Details Value References See Also
View source: R/feasibility_check.R
Takes as input a list of arguments (args) that define an adaptive trial (see buildTrial
or optimizeTrial
). This function adjusts the n_total
argument in order to find the smallest maximum sample size that meets the power constraints specified in the cases
argument.
1 2 | min_n_multistage(args, cases, trial_method, objective_fun, min_n = 1,
max_n = min_n * 1000, step_n = 10, showiter = FALSE)
|
args |
a list containing a subset of the arguments for the functions |
cases |
A list of power constraints, in the same format as those sent to |
trial_method |
either 'cov' or 'MB' for Maurer-Bretz (2013). |
objective_fun |
see |
min_n |
The smallest sample size to consider |
max_n |
The largest sample size to consider |
step_n |
The step size to consider when carrying out the binary search. For example, if |
showiter |
passed to |
This function requires that the objective function contain a 'base' element, and a 'power_diffs' element that is nonnegative when power constraints are met. For example, see min_E_SS_power_constraints
.
A list containing
n |
The smallest feasible n_total |
soln |
Output from |
.
Maurer, W. and F. Bretz (2013). Multiple testing in group sequential trials using graphical approaches. Statistics in Biopharmaceutical Research.
min_n_feasible
, feasibility_check
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