View source: R/f_simulation_multiarm_rates.R
getSimulationMultiArmRates | R Documentation |
Returns the simulated power, stopping and selection probabilities, conditional power, and expected sample size for testing rates in a multi-arm treatment groups testing situation.
getSimulationMultiArmRates(
design = NULL,
...,
activeArms = 3L,
effectMatrix = NULL,
typeOfShape = c("linear", "sigmoidEmax", "userDefined"),
piMaxVector = seq(0.2, 0.5, 0.1),
piControl = 0.2,
gED50 = NA_real_,
slope = 1,
intersectionTest = c("Dunnett", "Bonferroni", "Simes", "Sidak", "Hierarchical"),
directionUpper = TRUE,
adaptations = NA,
typeOfSelection = c("best", "rBest", "epsilon", "all", "userDefined"),
effectMeasure = c("effectEstimate", "testStatistic"),
successCriterion = c("all", "atLeastOne"),
epsilonValue = NA_real_,
rValue = NA_real_,
threshold = -Inf,
plannedSubjects = NA_real_,
allocationRatioPlanned = NA_real_,
minNumberOfSubjectsPerStage = NA_real_,
maxNumberOfSubjectsPerStage = NA_real_,
conditionalPower = NA_real_,
piTreatmentsH1 = NA_real_,
piControlH1 = NA_real_,
maxNumberOfIterations = 1000L,
seed = NA_real_,
calcSubjectsFunction = NULL,
selectArmsFunction = NULL,
showStatistics = FALSE
)
design |
The trial design. If no trial design is specified, a fixed sample size design is used.
In this case, Type I error rate |
... |
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed. |
activeArms |
The number of active treatment arms to be compared with control, default is |
effectMatrix |
Matrix of effect sizes with |
typeOfShape |
The shape of the dose-response relationship over the treatment groups.
This can be either |
piMaxVector |
Range of assumed probabilities for the treatment group with
highest response for |
piControl |
If specified, the assumed probability in the control arm for simulation and under which the sample size recalculation is performed. |
gED50 |
If |
slope |
If |
intersectionTest |
Defines the multiple test for the intersection
hypotheses in the closed system of hypotheses.
Five options are available in multi-arm designs: |
directionUpper |
Logical. Specifies the direction of the alternative,
only applicable for one-sided testing; default is |
adaptations |
A logical vector of length |
typeOfSelection |
The way the treatment arms or populations are selected at interim.
Five options are available: |
effectMeasure |
Criterion for treatment arm/population selection, either based on test statistic
( |
successCriterion |
Defines when the study is stopped for efficacy at interim.
Two options are available: |
epsilonValue |
For |
rValue |
For |
threshold |
Selection criterion: treatment arm / population is selected only if |
plannedSubjects |
|
allocationRatioPlanned |
The planned allocation ratio |
minNumberOfSubjectsPerStage |
When performing a data driven sample size recalculation,
the numeric vector |
maxNumberOfSubjectsPerStage |
When performing a data driven sample size recalculation,
the numeric vector |
conditionalPower |
If |
piTreatmentsH1 |
If specified, the assumed probability in the active treatment arm(s) under which the sample size recalculation is performed. |
piControlH1 |
If specified, the assumed probability in the reference group
(if different from |
maxNumberOfIterations |
The number of simulation iterations, default is |
seed |
The seed to reproduce the simulation, default is a random seed. |
calcSubjectsFunction |
Optionally, a function can be entered that defines the way of performing the sample size
recalculation. By default, sample size recalculation is performed with conditional power and specified
|
selectArmsFunction |
Optionally, a function can be entered that defines the way of how treatment arms
are selected. This function is allowed to depend on |
showStatistics |
Logical. If |
At given design the function simulates the power, stopping probabilities, selection probabilities, and expected sample size at given number of subjects, parameter configuration, and treatment arm selection rule in the multi-arm situation. An allocation ratio can be specified referring to the ratio of number of subjects in the active treatment groups as compared to the control group.
The definition of piTreatmentsH1
and/or piControlH1
makes only sense if kMax
> 1
and if conditionalPower
, minNumberOfSubjectsPerStage
, and
maxNumberOfSubjectsPerStage
(or calcSubjectsFunction
) are defined.
calcSubjectsFunction
This function returns the number of subjects at given conditional power and
conditional critical value for specified testing situation.
The function might depend on the variables
stage
,
selectedArms
,
directionUpper
,
plannedSubjects
,
allocationRatioPlanned
,
minNumberOfSubjectsPerStage
,
maxNumberOfSubjectsPerStage
,
conditionalPower
,
conditionalCriticalValue
,
overallRates
,
overallRatesControl
,
piTreatmentsH1
, and
piControlH1
.
The function has to contain the three-dots argument '...' (see examples).
Returns a SimulationResults
object.
The following generics (R generic functions) are available for this 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
.
## Not run:
# Simulate the power of the combination test with two interim stages and
# O'Brien & Fleming boundaries using Dunnett's intersection tests if the
# best treatment arm is selected at first interim. Selection only take
# place if a non-negative treatment effect is observed (threshold = 0);
# 20 subjects per stage and treatment arm, simulation is performed for
# four parameter configurations.
design <- getDesignInverseNormal(typeOfDesign = "OF")
effectMatrix <- matrix(c(0.2,0.2,0.2,
0.4,0.4,0.4,
0.4,0.5,0.5,
0.4,0.5,0.6),
byrow = TRUE, nrow = 4, ncol = 3)
x <- getSimulationMultiArmRates(design = design, typeOfShape = "userDefined",
effectMatrix = effectMatrix , piControl = 0.2,
typeOfSelection = "best", threshold = 0, intersectionTest = "Dunnett",
plannedSubjects = c(20, 40, 60),
maxNumberOfIterations = 50)
summary(x)
## End(Not run)
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