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.
The maximum number of stages K.
K = 1, 2, 3, ... (default is 3).
The maximum selectable kMax is 10 for group sequential or inverse normal and
6 for Fisher combination test designs.
The significance level alpha, default is 0.025.
Type II error rate, necessary for providing sample size calculations
(e.g., getSampleSizeMeans), beta spending function designs,
or optimum designs, default is 0.20.
Is the alternative one-sided (1) or two-sided (2), default is 1.
The information rates (that must be fixed prior to the trial),
default is (1:kMax) / kMax.
The futility bounds, defined on the test statistic z scale
(numeric vector of length kMax - 1).
The type of design. Type of design is one of the following:
O'Brien & Fleming ("OF"), Pocock ("P"), Wang & Tsiatis Delta class ("WT"),
Haybittle & Peto ("HP"), Optimum design within Wang & Tsiatis class ("WToptimum"),
O'Brien & Fleming type alpha spending ("asOF"), Pocock type alpha spending ("asP"),
Kim & DeMets alpha spending ("asKD"), Hwang, Shi & DeCani alpha spending ("asHSD"),
user defined alpha spending ("asUser"), default is "OF".
Delta for Wang & Tsiatis Delta class.
Optimization criterion for optimum design within
Wang & Tsiatis class ("ASNH1", "ASNIFH1",
"ASNsum"), default is "ASNH1", see details.
Parameter for alpha spending function.
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 ("bsOF", "bsP", "bsKD",
"bsHSD", "bsUser", default is "none").
The user defined alpha spending.
Numeric vector of length kMax containing the cumulative
alpha-spending (Type I error rate) up to each interim stage: 0 <= alpha_1 <= ... <= alpha_K <= alpha.
The user defined beta spending. Vector of length kMax containing the cumulative
beta-spending up to each interim stage.
Parameter for beta spending function.
If bindingFutility = TRUE is specified the calculation of
the critical values is affected by the futility bounds and the futility threshold is binding in the
sense that the study must be stopped if the futility condition was reached (default is FALSE).
The constant bounds up to stage kMax - 1 for the
Haybittle & Peto design (default is 3).
For two-sided testing, if twoSidedPower = TRUE is specified
the sample size calculation is performed by considering both tails of the distribution.
Default is FALSE, i.e., it is assumed that one tail probability is equal to 0 or the power
should be directed to one part.
The numerical tolerance, default is 1e-08.
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.
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.
getDesignSet for creating a set of designs to compare different designs.
Other design functions:
getDesignCharacteristics(),
getDesignConditionalDunnett(),
getDesignFisher(),
getDesignInverseNormal(),
getPowerAndAverageSampleNumber()
# NOT RUN {
# 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")
# }
Run the code above in your browser using DataLab