getDesignInverseNormal {rpact} | R Documentation |
Provides adjusted boundaries and defines a group sequential design for its use in the inverse normal combination test.
getDesignInverseNormal(
...,
kMax = NA_integer_,
alpha = NA_real_,
beta = NA_real_,
sided = 1L,
informationRates = NA_real_,
futilityBounds = NA_real_,
typeOfDesign = c("OF", "P", "WT", "PT", "HP", "WToptimum", "asP", "asOF", "asKD",
"asHSD", "asUser", "noEarlyEfficacy"),
deltaWT = NA_real_,
deltaPT1 = NA_real_,
deltaPT0 = 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,
betaAdjustment = 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
(e.g., |
sided |
Is the alternative one-sided ( |
informationRates |
The information rates t_1, ..., t_kMax (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. |
deltaPT1 |
Delta1 for Pampallona & Tsiatis class rejecting H0 boundaries. |
deltaPT0 |
Delta0 for Pampallona & Tsiatis class rejecting H1 boundaries. |
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 |
Logical. If |
betaAdjustment |
For two-sided beta spending designs, 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
,
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
.
getDesignSet()
for creating a set of designs to compare different designs.
Other design functions:
getDesignCharacteristics()
,
getDesignConditionalDunnett()
,
getDesignFisher()
,
getDesignGroupSequential()
,
getGroupSequentialProbabilities()
,
getPowerAndAverageSampleNumber()
# Calculate two-sided critical values for a four-stage
# Wang & Tsiatis design with Delta = 0.25 at level alpha = 0.05
getDesignInverseNormal(kMax = 4, alpha = 0.05, sided = 2,
typeOfDesign = "WT", deltaWT = 0.25)
# Defines a two-stage design at one-sided alpha = 0.025 with provision of early stopping
# if the one-sided p-value exceeds 0.5 at interim and no early stopping for efficacy.
# The futility bound is non-binding.
getDesignInverseNormal(kMax = 2, typeOfDesign = "noEarlyEfficacy", futilityBounds = 0)
## Not run:
# Calculate one-sided critical values and binding futility bounds for a three-stage
# design with alpha- and beta-spending functions according to Kim & DeMets with gamma = 2.5
# (planned informationRates as specified, default alpha = 0.025 and beta = 0.2)
getDesignInverseNormal(kMax = 3, informationRates = c(0.3, 0.75, 1),
typeOfDesign = "asKD", gammaA = 2.5, typeBetaSpending = "bsKD",
gammaB = 2.5, bindingFutility = TRUE)
## End(Not run)