getStageResults: Get Stage Results

In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

Get Stage Results

Description

Returns summary statistics and p-values for a given data set and a given design.

Usage

getStageResults(
  design,
  dataInput,
  ...,
  stage = NA_integer_,
  directionUpper = NA
)

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 (optional) arguments to be passed: thetaH0 The null hypothesis value, default is 0 for the normal and the binary case (testing means and rates, respectively), it is 1 for the survival case (testing the hazard ratio). For non-inferiority designs, thetaH0 is the non-inferiority bound. That is, in case of (one-sided) testing of means: a value != 0 (or a value != 1 for testing the mean ratio) can be specified. rates: a value != 0 (or a value != 1 for testing the risk ratio pi1 / pi2) can be specified. survival data: a bound for testing H0: hazard ratio = thetaH0 != 1 can be specified. For testing a rate in one sample, a value thetaH0 in (0, 1) has to be specified for defining the null hypothesis H0: pi = thetaH0. 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.
stage	The stage number (optional). Default: total number of existing stages in the data input.
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.

Details

Calculates and returns the stage results of the specified design and data input at the specified stage.

Value

Returns a StageResults 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(), getClosedCombinationTestResults(), getClosedConditionalDunnettTestResults(), getConditionalPower(), getConditionalRejectionProbabilities(), getFinalConfidenceInterval(), getFinalPValue(), getRepeatedConfidenceIntervals(), getRepeatedPValues(), getTestActions()

Examples

## Not run: 
design <- getDesignInverseNormal()
dataRates <- getDataset(
    n1      = c(10, 10),
    n2      = c(20, 20),
    events1 = c( 8, 10),
    events2 = c(10, 16))
getStageResults(design, dataRates)

## End(Not run)

rpact documentation built on Sept. 27, 2024, 5:08 p.m.