Description Usage Arguments Details Value
View source: R/feasibility_check.R
Functions to check feasibility of a design, adjust the design in order to make it feasible, or adjust the constraints so that they can be achieved. Here, "feasibility" means that there exists a 1stage design at the supplied sample size that meets the supplied power and Type I error constraints.
1 2 3 4 5 6 7 8  feasibility_check(FWER, p1, trial_method, r1 = 0.5, r2 = 1  r1, n_total,
cases, npoints_sqrt = 10, trial_args = list())
min_n_feasible(min_n = 100, max_n = min_n * 10, step_n = 5,
showiter = FALSE, trial_method, ...)
max_power_feasible(n_total, cases, p1, trial_method, step_multiplier = 0.01,
showiter = FALSE, FWER = 0.025, npoints_sqrt = 10, ...)

FWER 
the required familywise Type I error rate for the trial 
p1 
population proportion in subpopulation 1 
trial_method 
the type of trial to run. 'cov' for covariance based, 'MB' for Maurer Bretz, and 'covMB' for a combination approach. 
r1 
probability of being randomized to treatment in subpopulation 1 
r2 
probability of being randomized to treatment in subpopulation 2 
n_total 
the total sample size for the 1stage trial 
cases 
a list of power constraints, of the same format as those passed to 
npoints_sqrt 

trial_args 
either an empty list (for trial_method=='cov'), or a list containing graph edges for alpha reallocation (for trial_method=='MB'). 
min_n 
smallest sample size to consider for a 1stage trial 
max_n 
largest sample size to consider 
step_n 
the step size for sample size. For example, if 
showiter 
passed to 
... 
passed to 
step_multiplier 
the step size 
feasibility_check
checks whether it is possible to meet a set of power constraints while maintaining a given familywise Type I error rate (FWER) and total sample size.
min_n_feasible
implements a binary search to find the smallest sample size that meets the power constraints and FWER specified. In each iteration of the search, min_n_feasible
calls feasibility_check
.
Given a sample size and set of power cases, max_power_feasible
finds the number m between 0 and 1 such that if all of the minimum power thresholds are multiplied by m, then these power constraints will be satisfied at the supplied value of n_total
. In other words, it finds the factor by which the power constraints must be relaxed.
feasibility_check
returns a named vector, with the first element being 1 if the trial is feasible, and zero otherwise. The remaining elements of this vector tell the alpha allocated to each hypothesis in one particular setup that results in a feasible trial.
min_n_feasible
returns the smallest sample size that meets the power constraints and FWER specified, as well as the output from feasibility_check
at that sample size.
max_power_feasible
returns the multiplier m and a list of modified cases in which each power threshold has been multiplied by m.
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