rpact (version 4.2.0)

getConditionalRejectionProbabilities: Get Conditional Rejection Probabilities

Description

Calculates the conditional rejection probabilities (CRP) for given test results.

Usage

getConditionalRejectionProbabilities(stageResults, ...)

Value

Returns a numeric vector of length kMax or in case of multi-arm stage results a matrix (each column represents a stage, each row a comparison) containing the conditional rejection probabilities.

Arguments

stageResults

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

...

Further (optional) arguments to be passed:

iterations

Iterations for simulating the conditional rejection probabilities for Fisher's combination test. For checking purposes, it can be estimated via simulation with specified iterations.

seed

Seed for simulating the conditional rejection probabilities for Fisher's combination test. See above, default is a random seed.

Details

The conditional rejection probability is the probability, under H0, to reject H0 in one of the subsequent (remaining) stages. The probability is calculated using the specified design. For testing rates and the survival design, the normal approximation is used, i.e., it is calculated with the use of the prototype case testing a mean for normally distributed data with known variance.

The conditional rejection probabilities are provided up to the specified stage.

For Fisher's combination test, you can check the validity of the CRP calculation via simulation.

See Also

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

Examples

if (FALSE) {
# Calculate CRP for a Fisher's combination test design with
# two remaining stages and check the results by simulation.
design <- getDesignFisher(
    kMax = 4, alpha = 0.01,
    informationRates = c(0.1, 0.3, 0.8, 1)
)
data <- getDataset(n = c(40, 40), events = c(20, 22))
sr <- getStageResults(design, data, thetaH0 = 0.4)
getConditionalRejectionProbabilities(sr)
getConditionalRejectionProbabilities(sr,
    simulateCRP = TRUE,
    seed = 12345, iterations = 10000
)
}