Get Stage Results

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

Usage

getStageResults(design, dataInput, ..., 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 (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.

directionUpper

The direction of one-sided testing. Default is TRUE which means that larger values of the test statistics yield smaller p-values.

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.

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.

Details

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

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

design <- getDesignInverseNormal()
dataRates <- getDataset(
    n1      = c(10, 10),
    n2      = c(20, 20),
    events1 = c( 8, 10),
    events2 = c(10, 16))
getStageResults(design, dataRates)
#> Stage results of rates:
#> 
#> User defined parameters: not available
#> 
#> Default parameters:
#>   Stages                          : 1, 2, 3 
#>   Theta H0                        : 0 
#>   Direction                       : upper 
#>   Normal approximation            : TRUE 
#> 
#> Output:
#>   Overall test statistics         : 1.581, 2.064, NA 
#>   Overall p-values                : 0.05692, 0.01949, NA 
#>   Cumulative events (1)           : 8, 18, NA 
#>   Cumulative events (2)           : 10, 26, NA 
#>   Cumulative sample sizes (1)     : 10, 20, NA 
#>   Cumulative sample sizes (2)     : 20, 40, NA 
#>   Cumulative treatment rate       : 0.8, 0.9, NA 
#>   Cumulative control rate         : 0.5, 0.65, NA 
#>   Stage-wise test statistics      : 1.581, 1.519, NA 
#>   Stage-wise p-values             : 0.05692, 0.06437, NA 
#>   Cumulative effect sizes         : 0.30, 0.25, NA 
#>   Combination test statistics     : 1.581, 2.192, NA 
#>   Fixed weights                   : 0.577, 0.577, 0.577 
#> 
#> Legend:
#>   (i): values of treatment arm i