getRepeatedConfidenceIntervals: Get Repeated Confidence Intervals

In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

Get Repeated Confidence Intervals

Description

Calculates and returns the lower and upper limit of the repeated confidence intervals of the trial.

Usage

getRepeatedConfidenceIntervals(
  design,
  dataInput,
  ...,
  directionUpper = TRUE,
  tolerance = 1e-06,
  stage = NA_integer_
)

Arguments

design	The trial design.
dataInput	The summary data used for calculating the test results. This is either an element of DatasetMeans, of DatasetRates, or of DatasetSurvival and should be created with the function getDataset(). For more information see getDataset().
...	Further arguments to be passed to methods (cf., separate functions in "See Also" below), e.g., normalApproximation The type of computation of the p-values. Default is FALSE for testing means (i.e., the t test is used) and TRUE for testing rates and the hazard ratio. For testing rates, if normalApproximation = FALSE is specified, the binomial test (one sample) or the exact test of Fisher (two samples) is used for calculating the p-values. In the survival setting, normalApproximation = FALSE has no effect. equalVariances The type of t test. For testing means in two treatment groups, either the t test assuming that the variances are equal or the t test without assuming this, i.e., the test of Welch-Satterthwaite is calculated, default is TRUE. intersectionTest Defines the multiple test for the intersection hypotheses in the closed system of hypotheses when testing multiple hypotheses. Five options are available in multi-arm designs: "Dunnett", "Bonferroni", "Simes", "Sidak", and "Hierarchical", default is "Dunnett". Four options are available in population enrichment designs: "SpiessensDebois" (one subset only), "Bonferroni", "Simes", and "Sidak", default is "Simes". varianceOption Defines the way to calculate the variance in multiple treatment arms (> 2) or population enrichment designs for testing means. For multiple arms, three options are available: "overallPooled", "pairwisePooled", and "notPooled", default is "overallPooled". For enrichment designs, the options are: "pooled", "pooledFromFull" (one subset only), and "notPooled", default is "pooled". stratifiedAnalysis For enrichment designs, typically a stratified analysis should be chosen. For testing means and rates, also a non-stratified analysis based on overall data can be performed. For survival data, only a stratified analysis is possible (see Brannath et al., 2009), default is TRUE.
directionUpper	Logical. Specifies the direction of the alternative, only applicable for one-sided testing; default is TRUE which means that larger values of the test statistics yield smaller p-values.
tolerance	The numerical tolerance, default is 1e-06. Must be a positive numeric of length 1.
stage	The stage number (optional). Default: total number of existing stages in the data input.

Details

The repeated confidence interval at a given stage of the trial contains the parameter values that are not rejected using the specified sequential design. It can be calculated at each stage of the trial and can thus be used as a monitoring tool.

The repeated confidence intervals are provided up to the specified stage.

Value

Returns a matrix with 2 rows and kMax columns containing the lower RCI limits in the first row and the upper RCI limits in the second row, where each column represents a stage.

See Also

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

Examples

## Not run: 
design <- getDesignInverseNormal(kMax = 2)
data <- getDataset(
    n      = c( 20,  30),
    means  = c( 50,  51),
    stDevs = c(130, 140)
)
getRepeatedConfidenceIntervals(design, dataInput = data)

## End(Not run)

rpact documentation built on July 9, 2023, 6:30 p.m.