Get Design Conditional Dunnett Test

Defines the design to perform an analysis with the conditional Dunnett test.

Usage

getDesignConditionalDunnett(
  alpha = 0.025,
  informationAtInterim = 0.5,
  secondStageConditioning = TRUE
)

Arguments

alpha

The significance level alpha, default is 0.025. Must be a positive numeric of length 1.

informationAtInterim

The information to be expected at interim, default is informationAtInterim = 0.5.

secondStageConditioning

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 Koenig et al., 2008).

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 a data.frame,

• as.matrix() to coerce the object to a matrix.

Details

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 Koenig et al. (2008) and Wassmer & Brannath (2016), chapter 11 for details of the test procedure.

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

Other design functions: getDesignCharacteristics(), getDesignFisher(), getDesignGroupSequential(), getDesignInverseNormal(), getGroupSequentialProbabilities(), getPowerAndAverageSampleNumber()