getRepeatedConfidenceIntervals {rpact}  R Documentation 
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 = 1e06,
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 pvalues. 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 pvalues.
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 WelchSatterthwaite 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 multiarm 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 nonstratified 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 onesided testing; default is TRUE
which means that larger values of the test statistics yield smaller pvalues.

tolerance 
The numerical tolerance, default is 1e06 . 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:
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)
[Package
rpact version 3.3.4
Index]