getDesignFisher {rpact} | R Documentation |
Get Design Fisher
Description
Performs Fisher's combination test and returns critical values for this design.
Usage
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 = 0,
seed = NA_real_
)
Arguments
... |
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed. |
kMax |
The maximum number of stages |
alpha |
The significance level alpha, default is |
method |
|
userAlphaSpending |
The user defined alpha spending.
Numeric vector of length |
alpha0Vec |
Stopping for futility bounds for stage-wise p-values. |
informationRates |
The information rates t_1, ..., t_kMax (that must be fixed prior to the trial),
default is |
sided |
Is the alternative one-sided ( |
bindingFutility |
If |
tolerance |
The numerical tolerance, default is |
iterations |
The number of simulation iterations, e.g.,
|
seed |
Seed for simulating the power for Fisher's combination test. See above, default is a random seed. |
Details
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.
Value
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 adata.frame
, -
as.matrix()
to coerce the object to amatrix
.
How to get help for generic functions
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
.
See Also
getDesignSet()
for creating a set of designs to compare.
Other design functions:
getDesignCharacteristics()
,
getDesignConditionalDunnett()
,
getDesignGroupSequential()
,
getDesignInverseNormal()
,
getGroupSequentialProbabilities()
,
getPowerAndAverageSampleNumber()
Examples
# 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)