| getConditionalPower {rpact} | R Documentation |
Calculates and returns the conditional power.
getConditionalPower(stageResults, ..., nPlanned, allocationRatioPlanned = 1)
stageResults |
The results at given stage, obtained from |
... |
Further (optional) arguments to be passed:
|
nPlanned |
The additional (i.e., "new" and not cumulative) sample size planned for each of the subsequent stages. The argument must be a vector with length equal to the number of remaining stages and contain the combined sample size from both treatment groups if two groups are considered. For survival outcomes, it should contain the planned number of additional events. For multi-arm designs, it is the per-comparison (combined) sample size. For enrichment designs, it is the (combined) sample size for the considered sub-population. |
allocationRatioPlanned |
The planned allocation ratio |
The conditional power is calculated if the planned sample size for the subsequent stages is specified.
For testing rates in a two-armed trial, pi1 and pi2 typically refer to the rates in the treatment
and the control group, respectively. This is not mandatory, however, and so pi1 and pi2 can be interchanged.
In many-to-one multi-armed trials, piTreatments and piControl refer to the rates in the treatment arms and
the one control arm, and so they cannot be interchanged. piTreatments and piControls in enrichment designs
can principally be interchanged, but we use the plural form to indicate that the rates can be differently
specified for the sub-populations.
For Fisher's combination test, the conditional power for more than one remaining stages is estimated via simulation.
Returns a ConditionalPowerResults object.
The following generics (R generic functions) are available for this result 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.
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.
plot.StageResults() or plot.AnalysisResults()
for plotting the conditional power.
Other analysis functions:
getAnalysisResults(),
getClosedCombinationTestResults(),
getClosedConditionalDunnettTestResults(),
getConditionalRejectionProbabilities(),
getFinalConfidenceInterval(),
getFinalPValue(),
getRepeatedConfidenceIntervals(),
getRepeatedPValues(),
getStageResults(),
getTestActions()
## Not run:
data <- getDataset(
n1 = c(22, 13, 22, 13),
n2 = c(22, 11, 22, 11),
means1 = c(1, 1.1, 1, 1),
means2 = c(1.4, 1.5, 1, 2.5),
stds1 = c(1, 2, 2, 1.3),
stds2 = c(1, 2, 2, 1.3)
)
stageResults <- getStageResults(
getDesignGroupSequential(kMax = 4),
dataInput = data, stage = 2, directionUpper = FALSE
)
getConditionalPower(stageResults, thetaH1 = -0.4,
nPlanned = c(64, 64), assumedStDev = 1.5,
allocationRatioPlanned = 3
)
## End(Not run)