getSimulationMultiArmMeans {rpact}  R Documentation 
Returns the simulated power, stopping and selection probabilities, conditional power, and expected sample size for testing means in a multiarm treatment groups testing situation.
getSimulationMultiArmMeans(
design = NULL,
...,
activeArms = 3L,
effectMatrix = NULL,
typeOfShape = c("linear", "sigmoidEmax", "userDefined"),
muMaxVector = seq(0, 1, 0.2),
gED50 = NA_real_,
slope = 1,
intersectionTest = c("Dunnett", "Bonferroni", "Simes", "Sidak", "Hierarchical"),
stDev = 1,
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_integer_,
allocationRatioPlanned = NA_real_,
minNumberOfSubjectsPerStage = NA_real_,
maxNumberOfSubjectsPerStage = NA_real_,
conditionalPower = NA_real_,
thetaH1 = NA_real_,
stDevH1 = 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 doseresponse relationship over the treatment groups.
This can be either 
muMaxVector 
Range of effect sizes for the treatment group with highest response
for 
gED50 
If 
slope 
If 
intersectionTest 
Defines the multiple test for the intersection
hypotheses in the closed system of hypotheses.
Five options are available in multiarm designs: 
stDev 
The standard deviation under which the data is simulated,
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 
thetaH1 
If specified, the value of the alternative under which the conditional power or sample size recalculation calculation is performed. Must be a numeric of length 1. 
stDevH1 
If specified, the value of the standard deviation under which
the conditional power or sample size recalculation calculation is performed,
default is the value of 
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 with 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 multiarm 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 thetaH1
and/or stDevH1
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
,
plannedSubjects
,
allocationRatioPlanned
,
minNumberOfSubjectsPerStage
,
maxNumberOfSubjectsPerStage
,
conditionalPower
,
conditionalCriticalValue
,
overallEffects
, and
stDevH1
.
The function has to contain the threedots 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
.
# Assess a treatmentarm selection strategy with three active arms,
# if the better of the arms is selected for the second stage, and
# compare it with the noselection case.
# Assume a linear doseresponse relationship
maxNumberOfIterations < 100
designIN < getDesignInverseNormal(typeOfDesign = "OF", kMax = 2)
sim < getSimulationMultiArmMeans(design = designIN,
activeArms = 3, typeOfShape = "linear",
muMaxVector = seq(0,0.8,0.2),
intersectionTest = "Simes",
typeOfSelection = "best",
plannedSubjects = c(30,60),
maxNumberOfIterations = maxNumberOfIterations)
sim0 < getSimulationMultiArmMeans(design = designIN,
activeArms = 3, typeOfShape = "linear",
muMaxVector = seq(0,0.8,0.2),
intersectionTest = "Simes",
typeOfSelection = "all",
plannedSubjects = c(30,60),
maxNumberOfIterations = maxNumberOfIterations)
sim$rejectAtLeastOne
sim$expectedNumberOfSubjects
sim0$rejectAtLeastOne
sim0$expectedNumberOfSubjects
# Compare the power of the conditional Dunnett test with the power of the
# combination test using Dunnett's intersection tests if no treatment arm
# selection takes place. Asseume a linear doseresponse relationship.
maxNumberOfIterations < 100
designIN < getDesignInverseNormal(typeOfDesign = "asUser",
userAlphaSpending = c(0, 0.025))
designCD < getDesignConditionalDunnett(secondStageConditioning = TRUE)
index < 1
for (design in c(designIN, designCD)) {
results < getSimulationMultiArmMeans(design, activeArms = 3,
muMaxVector = seq(0, 1, 0.2), typeOfShape = "linear",
plannedSubjects = cumsum(rep(20, 2)),
intersectionTest = "Dunnett",
typeOfSelection = "all", maxNumberOfIterations = maxNumberOfIterations)
if (index == 1) {
drift < results$effectMatrix[nrow(results$effectMatrix), ]
plot(drift, results$rejectAtLeastOne, type = "l", lty = 1,
lwd = 3, col = "black", ylab = "Power")
} else {
lines(drift,results$rejectAtLeastOne, type = "l",
lty = index, lwd = 3, col = "red")
}
index < index + 1
}
legend("topleft", legend=c("Combination Dunnett", "Conditional Dunnett"),
col=c("black", "red"), lty = (1:2), cex = 0.8)
# Assess the design characteristics of a user defined selection
# strategy in a twostage design using the inverse normal method
# with constant bounds. Stopping for futility due to
# deselection of all treatment arms.
designIN < getDesignInverseNormal(typeOfDesign = "P", kMax = 2)
mySelection < function(effectVector) {
selectedArms < (effectVector >= c(0, 0.1, 0.3))
return(selectedArms)
}
results < getSimulationMultiArmMeans(designIN, activeArms = 3,
muMaxVector = seq(0, 1, 0.2),
typeOfShape = "linear",
plannedSubjects = c(30,60),
intersectionTest = "Dunnett",
typeOfSelection = "userDefined",
selectArmsFunction = mySelection,
maxNumberOfIterations = 100)
options(rpact.summary.output.size = "medium")
summary(results)
if (require(ggplot2)) plot(results, type = c(5,3,9), grid = 4)