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Calculates and returns the conditional power.
getConditionalPower(stageResults, ..., nPlanned, allocationRatioPlanned = 1)The results at given stage, obtained from getStageResults.
Further (optional) arguments to be passed:
thetaH1 and assumedStDevs or piTreatments, piControlThe
assumed effect size or assumed rates to calculate the conditional power in multi-arm trials
or enrichment designs. For survival designs, thetaH1 refers to the hazard ratio.
You can specify a value or a vector with elements referring to the
treatment arms or the sub-populations, respectively.
For testing means, an assumed standard deviation can be specified, default is 1.
iterationsIterations for simulating the power for Fisher's combination test.
If the power for more than one remaining stages is to be determined for Fisher's combination test,
it is estimated via simulation with specified
iterations, the default value is 10000.
seedSeed for simulating the power for Fisher's combination test. See above, default is a random seed.
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.
The planned allocation ratio n1 / n2 for a two treatment groups
design, default is 1. For multi-arm designs, it is the allocation ratio relating the active arm(s) to the control.
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,
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.
The conditional power is calculated only if the effect size and the sample size is specified.
For Fisher's combination test, the conditional power for more than one remaining stages is estimated via simulation.
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 {
design <- getDesignInverseNormal(kMax = 2)
data1 <- getDataset(
n = c(20, 30),
means = c(50, 51),
stDevs = c(130, 140)
)
data2 <- 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 = data2, stage = 2, directionUpper = FALSE)
getConditionalPower(stageResults, thetaH1 = -0.4,
nPlanned = c(64, 64), assumedStDev = 1.5, allocationRatioPlanned = 3)
# }
# NOT RUN {
# }
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