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,
tolerance = 1e-14,
iterations = 0L,
seed = NA_real_
)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.
K = 1, 2, 3, ... (default 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.
"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 (that must be fixed prior to the trial),
default is (1:kMax) / kMax.
Is the alternative one-sided (1) or two-sided (2), default is 1.
If bindingFutility = TRUE is specified the calculation of
the critical values is affected by the futility bounds (default is TRUE).
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 default 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.
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,
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(),
getPowerAndAverageSampleNumber()
# NOT RUN {
# 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)
# }
Run the code above in your browser using DataLab