Get Closed Combination Test Results

Calculates and returns the results from the closed combination test in multi-arm and population enrichment designs.

Usage

getClosedCombinationTestResults(stageResults)

Arguments

stageResults

The results at given stage, obtained from getStageResults().

Value

Returns a ClosedCombinationTestResults 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.

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 analysis functions: getAnalysisResults(), getClosedConditionalDunnettTestResults(), getConditionalPower(), getConditionalRejectionProbabilities(), getFinalConfidenceInterval(), getFinalPValue(), getRepeatedConfidenceIntervals(), getRepeatedPValues(), getStageResults(), getTestActions()

Examples

if (FALSE) { # \dontrun{
# In a four-stage combination test design with O'Brien & Fleming boundaries
# at the first stage the second treatment arm was dropped. With the Bonferroni
# intersection test, the results of a closed adaptive test procedure are
# obtained as follows with the given data (treatment arm 4 refers to the
# reference group):
data <- getDataset(
    n1 = c(22, 23),
    n2 = c(21, NA),
    n3 = c(20, 25),
    n4 = c(25, 27),
    means1 = c(1.63, 1.51),
    means2 = c(1.4, NA),
    means3 = c(0.91, 0.95),
    means4 = c(0.83, 0.75),
    stds1 = c(1.2, 1.4),
    stds2 = c(1.3, NA),
    stds3 = c(1.1, 1.14),
    stds4 = c(1.02, 1.18)
)

design <- getDesignInverseNormal(kMax = 4)
stageResults <- getStageResults(design,
    dataInput = data,
    intersectionTest = "Bonferroni"
)
getClosedCombinationTestResults(stageResults)
} # }