OneStageDesign
implements a one-stage design as special case of
a two-stage design, i.e. as sub-class of TwoStageDesign
.
This is possible by defining n2 = 0,
c = c1f = c1e,
c2(x1) = ifelse(x1< c, Inf, -Inf).
No integration pivots etc are required (set to NaN
).
OneStageDesign(n, ...)
# S4 method for numeric
OneStageDesign(n, c, event_rate)
# S4 method for OneStageDesign
TwoStageDesign(n1, event_rate, order = 5L, eps = 0.01, ...)
# S4 method for OneStageDesignSurvival
TwoStageDesign(n1, order = 5L, eps = 0.01, ...)
# S4 method for OneStageDesign
plot(x, y, ...)
sample size (stage-one sample size)
further optional arguments
rejection boundary (c = c1f = c1e)
probability that a subject in either group will eventually have an event, only needs to be specified for time-to-event endpoints.
OneStageDesign
object to convert, overloaded from
TwoStageDesign
integer >= 2, default is 5; order of Gaussian quadrature integration rule to use for new TwoStageDesign.
numeric > 0, default = .01; the single critical value c must be split in a continuation interval [c1f, c1e]; this is given by c +/- eps.
design to plot
not used
Note that the default plot,TwoStageDesign-method
method
is not supported for OneStageDesign
objects.
design <- OneStageDesign(30, 1.96)
summary(design)
#> OneStageDesign: n1 = 30
#>
futility | continue | efficacy
#>
x1: 1.96 | NaN | 1.96
#>
c2(x1): +Inf | NA | -Inf
#>
n2(x1): 0 | 0 | 0
#>
design_twostage <- TwoStageDesign(design)
summary(design_twostage)
#> TwoStageDesign: n1 = 30
#>
futility | continue | efficacy
#>
x1: 1.95 | 1.95 1.95 1.96 1.97 1.97 | 1.97
#>
c2(x1): +Inf | +3.00 +3.00 +0.00 -3.00 -3.00 | -Inf
#>
n2(x1): 0 | 0 0 0 0 0 | 0
#>
design_survival <- OneStageDesign(30,1.96,0.7)
TwoStageDesign(design_survival)
#> TwoStageDesignSurvival<n_events1=30;1.9<=x1<=2.0;n_events2=0>