getDesignGroupSequential {rpact} | R Documentation |
Provides adjusted boundaries and defines a group sequential design.
getDesignGroupSequential( ..., kMax = NA_integer_, alpha = NA_real_, beta = NA_real_, sided = 1, informationRates = NA_real_, futilityBounds = NA_real_, typeOfDesign = c("OF", "P", "WT", "HP", "WToptimum", "asP", "asOF", "asKD", "asHSD", "asUser"), deltaWT = NA_real_, optimizationCriterion = c("ASNH1", "ASNIFH1", "ASNsum"), gammaA = NA_real_, typeBetaSpending = c("none", "bsP", "bsOF", "bsKD", "bsHSD", "bsUser"), userAlphaSpending = NA_real_, userBetaSpending = NA_real_, gammaB = NA_real_, bindingFutility = NA, constantBoundsHP = 3, twoSidedPower = NA, tolerance = 1e-08 )
... |
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed. |
kMax |
The maximum number of stages |
alpha |
The significance level alpha, default is |
beta |
Type II error rate, necessary for providing sample size calculations |
sided |
Is the alternative one-sided ( |
informationRates |
The information rates (that must be fixed prior to the trial),
default is |
futilityBounds |
The futility bounds, defined on the test statistic z scale
(numeric vector of length |
typeOfDesign |
The type of design. Type of design is one of the following:
O'Brien & Fleming ( |
deltaWT |
Delta for Wang & Tsiatis Delta class. |
optimizationCriterion |
Optimization criterion for optimum design within
Wang & Tsiatis class ( |
gammaA |
Parameter for alpha spending function. |
typeBetaSpending |
Type of beta spending. Type of of beta spending is one of the following:
O'Brien & Fleming type beta spending, Pocock type beta spending,
Kim & DeMets beta spending, Hwang, Shi & DeCani beta spending, user defined
beta spending ( |
userAlphaSpending |
The user defined alpha spending.
Numeric vector of length |
userBetaSpending |
The user defined beta spending. Vector of length |
gammaB |
Parameter for beta spending function. |
bindingFutility |
If |
constantBoundsHP |
The constant bounds up to stage |
twoSidedPower |
For two-sided testing, if |
tolerance |
The numerical tolerance, default is |
Depending on typeOfDesign
some parameters are specified, others not.
For example, only if typeOfDesign
"asHSD"
is selected, gammaA
needs to be specified.
If an alpha spending approach was specified ("asOF"
, "asP"
, "asKD"
, "asHSD"
, or "asUser"
)
additionally a beta spending function can be specified to produce futility bounds.
For optimum designs, "ASNH1"
minimizes the expected sample size under H1,
"ASNIFH1"
minimizes the sum of the maximum sample and the expected sample size under H1,
and "ASNsum"
minimizes the sum of the maximum sample size, the expected sample size under a value midway H0 and H1,
and the expected sample size under H1.
Returns a TrialDesign
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
.
getDesignSet
for creating a set of designs to compare different designs.
Other design functions:
getDesignCharacteristics()
,
getDesignConditionalDunnett()
,
getDesignFisher()
,
getDesignInverseNormal()
,
getPowerAndAverageSampleNumber()
# Calculate two-sided critical values for a four-stage # Wang & Tsiatis design with Delta = 0.25 at level alpha = 0.05 getDesignGroupSequential(kMax = 4, sided = 2, typeOfDesign = "WT", deltaWT = 0.25) # Calculate the Pocock type alpha spending critical values if the second # interim analysis was performed after 70% of the maximum information was observed getDesignGroupSequential(informationRates = c(0.4, 0.7), typeOfDesign = "asP")