Performs Fisher's combination test and returns critical values for this design.
getDesignFisher(
...,
kMax = NA_integer_,
alpha = NA_real_,
method = c("equalAlpha", "fullAlpha", "noInteraction", "userDefinedAlpha"),
userAlphaSpending = NA_real_,
alpha0Vec = NA_real_,
informationRates = NA_real_,
sided = 1,
bindingFutility = NA,
directionUpper = NA,
tolerance = 1e-14,
iterations = 0,
seed = NA_real_
)Returns a TrialDesign object.
The following generics (R generic functions) are available for this result object:
names() to obtain the field names,
print() to print the object,
summary() to display a summary of the object,
plot() to plot the object,
as.data.frame() to coerce the object to a data.frame,
as.matrix() to coerce the object to a matrix.
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
The maximum number of stages K.
Must be 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.
The significance level alpha, default is 0.025. Must be a positive numeric of length 1.
"equalAlpha", "fullAlpha", "noInteraction", or "userDefinedAlpha",
default is "equalAlpha" (for details, see Wassmer, 1999).
The 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.
Stopping for futility bounds for stage-wise p-values.
The information rates t_1, ..., t_kMax (that must be fixed prior to the trial),
default is (1:kMax) / kMax. For the weighted inverse normal design, the weights are derived
through w_1 = sqrt(t_1), and w_k = sqrt(t_k - t_(k-1)). For the weighted Fisher's combination test, the
weights (scales) are w_k = sqrt((t_k - t_(k-1)) / t_1) (see the documentation).
Is the alternative one-sided (1) or two-sided (2), default is 1.
Must be a positive integer of length 1.
If bindingFutility = TRUE is specified the calculation of
the critical values is affected by the futility bounds (default is TRUE).
Logical. Specifies the direction of the alternative,
only applicable for one-sided testing; default is TRUE
which means that larger values of the test statistics yield smaller p-values.
The numerical tolerance, default is 1e-14.
The number of simulation iterations, e.g.,
getDesignFisher(iterations = 100000) checks the validity of the critical values for the design.
The default value of iterations is 0, i.e., no simulation will be executed.
Seed for simulating the power for Fisher's combination test. See above, default is a random seed.
Click on the link of a generic in the list above to go directly to the help documentation of
the rpact specific implementation of the generic.
Note that you can use the R function methods to get all the methods of a generic and
to identify the object specific name of it, e.g.,
use methods("plot") to get all the methods for the plot generic.
There you can find, e.g., plot.AnalysisResults and
obtain the specific help documentation linked above by typing ?plot.AnalysisResults.
getDesignFisher() calculates the critical values and stage levels for
Fisher's combination test as described in Bauer (1989), Bauer and Koehne (1994),
Bauer and Roehmel (1995), and Wassmer (1999) for equally and unequally sized stages.
getDesignSet() for creating a set of designs to compare.
Other design functions:
getDesignCharacteristics(),
getDesignConditionalDunnett(),
getDesignGroupSequential(),
getDesignInverseNormal(),
getGroupSequentialProbabilities(),
getPowerAndAverageSampleNumber()
if (FALSE) {
# Calculate critical values for a two-stage Fisher's combination test
# with full level alpha = 0.05 at the final stage and stopping for
# futility bound alpha0 = 0.50, as described in Bauer and Koehne (1994).
getDesignFisher(kMax = 2, method = "fullAlpha", alpha = 0.05, alpha0Vec = 0.50)
}
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