Defines the design to perform an analysis with the conditional Dunnett test.
getDesignConditionalDunnett(
alpha = 0.025,
informationAtInterim = 0.5,
secondStageConditioning = TRUE
)The significance level alpha, default is 0.025.
The information to be expected at interim, default is informationAtInterim = 0.5.
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
(for details, see K<U+00F6>nig et al., 2008).
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.
For performing the conditional Dunnett test the design must be defined through this function.
You can define the information fraction and the way of how to compute the second stage
p-values only in the design definition, and not in the analysis call.
See getClosedConditionalDunnettTestResults for an example and K<U+00F6>nig et al. (2008) and
Wassmer & Brannath (2016), chapter 11 for details of the test procedure.
Other design functions:
getDesignCharacteristics(),
getDesignFisher(),
getDesignGroupSequential(),
getDesignInverseNormal(),
getPowerAndAverageSampleNumber()