Calculates the characteristics of a design and returns it.
Arguments
- design
A conventional
TrialDesignobject or an optimal conditional error design created withgetDesignOptimalConditionalError(). If omitted, a default conventional design is created.- ...
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
Value
For an optimal conditional error design, a
TrialDesignOptimalConditionalErrorCharacteristics parameter set with
theta, overallReject, rejectPerStage (two rows, one per stage), and
futilityPerStage (one row for interim futility). Matrix columns correspond
to entries in theta, including for a single effect. Final non-rejection
is not counted as early futility. overallReject is overall power under
an alternative and the type I error probability at theta = 0.
See getDesignOptimalConditionalError() for the method and its assumptions.
For conventional designs, returns a TrialDesignCharacteristics 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 adata.frame,as.matrix()to coerce the object to amatrix.
Details
Calculates the inflation factor (IF), the expected reduction in sample size under H1, under H0, and under a value in between H0 and H1. Furthermore, absolute information values are calculated under the prototype case testing H0: mu = 0 against H1: mu = 1.
For an optimal conditional error design, supply theta through ... as a
finite numeric vector of treatment effects on the mean difference scale.
The design is evaluated at those effects without recalibration.
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.
References
Brannath, W. & Bauer, P. (2004). Optimal conditional error functions for the control of conditional power. Biometrics, 60(3), 715-723. doi:10.1111/j.0006-341X.2004.00221.x
Brannath, W., zur Verth, J., Dreher, M. & Scharpenberg, M. (2024; revised 2026). Optimal monotone conditional error functions. doi:10.48550/arXiv.2402.00814
The related R package optconerrf: Optimal Monotone Conditional Error Functions provides a standalone implementation of this methodology. The rpact implementation uses rpact design objects and interfaces; optconerrf is not required to run these functions.
See also
Optimal Conditional Error Designs with rpact for a worked example with stopping rules, information constraints, interim estimates, and conditional power functions.
Other design functions:
getDesignConditionalDunnett(),
getDesignFisher(),
getDesignFixed(),
getDesignGroupSequential(),
getDesignInverseNormal(),
getGroupSequentialProbabilities(),
getPowerAndAverageSampleNumber()
Examples
design <- getDesignOptimalConditionalError(
alpha = 0.025, efficacyBounds = 0.001, futilityBounds = 0.5, conditionalPower = 0.9,
thetaH1 = 0.5, useInterimEstimate = FALSE, firstStageInformation = 4,
likelihoodRatioDistribution = "maxlr"
)
getDesignCharacteristics(design, theta = c(0, 0.25, 0.5))
#> User defined parameters:
#> Effect : 0.00, 0.25, 0.50
#>
#> Operating characteristics:
#> Overall reject : 0.0250, 0.2927, 0.7590
#> Reject per stage [1] : 0.001000, 0.004796, 0.018298
#> Reject per stage [2] : 0.024000, 0.287861, 0.740742
#> Futility stop per stage : 0.5000, 0.3085, 0.1587
#>
if (FALSE) { # \dontrun{
# Calculate design characteristics for a three-stage O'Brien & Fleming
# design at power 90% and compare it with Pocock's design.
getDesignCharacteristics(getDesignGroupSequential(beta = 0.1))
getDesignCharacteristics(getDesignGroupSequential(beta = 0.1, typeOfDesign = "P"))
} # }