Trial design for conditional Dunnett tests.
kMaxThe maximum number of stages K.
Is a positive integer of length 1 (default value is 3).
The maximum selectable kMax is 20 for group sequential or inverse normal and
6 for Fisher combination test designs.
alphaThe significance level alpha, default is 0.025.
Is a positive numeric of length 1.
stagesThe stage numbers of the trial. Is an integer vector of length kMax.
informationRatesThe information rates (that must be fixed prior to the trial),
default is (1:kMax) / kMax. Is a numeric vector of length kMax (the maximum number of stages).
userAlphaSpendingThe user defined alpha spending.
Numeric vector of length kMax containing the cumulative
alpha-spending (Type I error rate) up to each interim stage: 0 <= alpha_1 <= ... <= alpha_K <= alpha.
criticalValuesThe critical values for each stage of the trial.
Is a numeric vector of length kMax (the maximum number of stages).
stageLevelsThe levels for each stage.
alphaSpentThe cumulative alpha spent at each stage.
Is a numeric vector with length kMax (the maximum number of stages).
bindingFutilityLogical. If bindingFutility = TRUE is specified the calculation of
the critical values is affected by the futility bounds and the futility threshold is binding in the
sense that the study must be stopped if the futility condition was reached (default is FALSE).
toleranceThe numerical tolerance, default is 1e-06. Is a positive numeric of length 1.
informationAtInterimThe information to be expected at interim, default is informationAtInterim = 0.5. Is a numeric vector of length 1.
secondStageConditioningLogical. The way the second stage p-values are calculated within the closed system of hypotheses. If secondStageConditioning = FALSE is specified, the unconditional adjusted p-values are used, otherwise conditional adjusted p-values are calculated, default is secondStageConditioning = TRUE.
sidedIs the alternative one-sided (1) or two-sided (2), default is 1.
Is a positive integer of length 1.
This object should not be created directly; use getDesignConditionalDunnett()
with suitable arguments to create a conditional Dunnett test design.
getDesignConditionalDunnett() for creating a conditional Dunnett test design.