Returns the simulated power, stopping and selection probabilities, conditional power, and
expected sample size for testing hazard ratios in a multi-arm treatment groups testing situation.
In contrast to getSimulationSurvival() (where survival times are simulated), normally
distributed logrank test statistics are simulated.
getSimulationMultiArmSurvival(
design = NULL,
...,
activeArms = 3L,
effectMatrix = NULL,
typeOfShape = c("linear", "sigmoidEmax", "userDefined"),
omegaMaxVector = seq(1, 2.6, 0.4),
gED50 = NA_real_,
slope = 1,
intersectionTest = c("Dunnett", "Bonferroni", "Simes", "Sidak", "Hierarchical"),
directionUpper = NA,
adaptations = NA,
typeOfSelection = c("best", "rBest", "epsilon", "all", "userDefined"),
effectMeasure = c("effectEstimate", "testStatistic"),
successCriterion = c("all", "atLeastOne"),
correlationComputation = c("alternative", "null"),
epsilonValue = NA_real_,
rValue = NA_real_,
threshold = -Inf,
plannedEvents = NA_real_,
allocationRatioPlanned = NA_real_,
minNumberOfEventsPerStage = NA_real_,
maxNumberOfEventsPerStage = NA_real_,
conditionalPower = NA_real_,
thetaH1 = NA_real_,
maxNumberOfIterations = 1000L,
seed = NA_real_,
calcEventsFunction = NULL,
selectArmsFunction = NULL,
showStatistics = FALSE
)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.
The trial design. If no trial design is specified, a fixed sample size design is used.
In this case, Type I error rate alpha, Type II error rate beta, twoSidedPower,
and sided can be directly entered as argument where necessary.
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
The number of active treatment arms to be compared with control, default is 3.
Matrix of effect sizes with activeArms columns and number of rows
reflecting the different situations to consider.
The shape of the dose-response relationship over the treatment groups.
This can be either "linear", "sigmoidEmax", or "userDefined",
default is "linear".
For "linear", "muMaxVector" specifies the range
of effect sizes for the treatment group with highest response.
If "sigmoidEmax" is selected, "gED50" and "slope" has to be entered
to specify the ED50 and the slope of the sigmoid Emax model.
For "sigmoidEmax", "muMaxVector" specifies the range
of effect sizes for the treatment group with response according to infinite dose.
If "userDefined" is selected, "effectMatrix" has to be entered.
Range of hazard ratios with highest response for "linear" and
"sigmoidEmax" model, default is seq(1, 2.6, 0.4).
If typeOfShape = "sigmoidEmax" is selected, "gED50" has to be entered
to specify the ED50 of the sigmoid Emax model.
If typeOfShape = "sigmoidEmax" is selected, "slope" can be entered
to specify the slope of the sigmoid Emax model, default is 1.
Defines the multiple test for the intersection
hypotheses in the closed system of hypotheses.
Five options are available in multi-arm designs: "Dunnett", "Bonferroni", "Simes",
"Sidak", and "Hierarchical", default is "Dunnett".
Logical. Specifies the direction of the alternative,
only applicable for one-sided testing; default is TRUE
which means that larger values of the test statistics yield smaller p-values.
A logical vector of length kMax - 1 indicating whether or not an adaptation takes
place at interim k, default is rep(TRUE, kMax - 1).
The way the treatment arms or populations are selected at interim.
Five options are available: "best", "rbest", "epsilon", "all", and "userDefined",
default is "best".
For "rbest" (select the rValue best treatment arms/populations), the parameter rValue has to be specified,
for "epsilon" (select treatment arm/population not worse than epsilon compared to the best), the parameter
epsilonValue has to be specified.
If "userDefined" is selected, "selectArmsFunction" or "selectPopulationsFunction" has to be specified.
Criterion for treatment arm/population selection, either based on test statistic
("testStatistic") or effect estimate (difference for means and rates or ratio for survival) ("effectEstimate"),
default is "effectEstimate".
Defines when the study is stopped for efficacy at interim.
Two options are available: "all" stops the trial
if the efficacy criterion is fulfilled for all selected treatment arms/populations,
"atLeastOne" stops if at least one of the selected treatment arms/populations is shown to be
superior to control at interim, default is "all".
If correlationComputation = "alternative",
for simulating log-rank statistics in the many-to-one design, a correlation
matrix according to Deng et al. (Biometrics, 2019) accounting for the
respective alternative is used;
if correlationComputation = "null", a constant correlation matrix valid
under the null, i.e., not accounting for the alternative is used,
default is "alternative".
For typeOfSelection = "epsilon" (select treatment arm / population not worse than
epsilon compared to the best), the parameter epsilonValue has to be specified. Must be a numeric of length 1.
For typeOfSelection = "rbest" (select the rValue best treatment arms / populations),
the parameter rValue has to be specified.
Selection criterion: treatment arm / population is selected only if effectMeasure
exceeds threshold, default is -Inf.
threshold can also be a vector of length activeArms referring to
a separate threshold condition over the treatment arms.
plannedEvents is a numeric vector of length kMax (the number of stages of the design)
that determines the number of cumulated (overall) events in survival designs when the interim stages are planned.
For two treatment arms, it is the number of events for both treatment arms.
For multi-arm designs, plannedEvents refers to the overall number of events for the selected arms plus control.
The planned allocation ratio n1 / n2 for a two treatment groups
design, default is 1. For multi-arm designs, it is the allocation ratio relating the active arm(s) to the control.
For simulating means and rates for a two treatment groups design, it can be a vector of length kMax, the number of stages.
It can be a vector of length kMax, too, for multi-arm and enrichment designs.
In these cases, a change of allocating subjects to treatment groups over the stages can be assessed.
Note that internally allocationRatioPlanned is treated as a vector of length kMax, not a scalar.
When performing a data driven sample size recalculation,
the numeric vector minNumberOfEventsPerStage with length kMax determines the
minimum number of events per stage (i.e., not cumulated), the first element
is not taken into account.
When performing a data driven sample size recalculation,
the numeric vector maxNumberOfEventsPerStage with length kMax determines the maximum number
of events per stage (i.e., not cumulated), the first element is not taken into account.
If conditionalPower together with minNumberOfSubjectsPerStage and
maxNumberOfSubjectsPerStage (or minNumberOfEventsPerStage and maxNumberOfEventsPerStage
for survival designs) is specified, a sample size recalculation based on the specified conditional power is performed.
It is defined as the power for the subsequent stage given the current data. By default,
the conditional power will be calculated under the observed effect size. Optionally, you can also specify thetaH1 and
stDevH1 (for simulating means), pi1H1 and pi2H1 (for simulating rates), or thetaH1 (for simulating
hazard ratios) as parameters under which it is calculated and the sample size recalculation is performed.
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.
The number of simulation iterations, default is 1000. Must be a positive integer of length 1.
The seed to reproduce the simulation, default is a random seed.
Optionally, a function can be entered that defines the way of performing the sample size
recalculation. By default, event number recalculation is performed with conditional power and specified
minNumberOfEventsPerStage and maxNumberOfEventsPerStage (see details and examples).
Optionally, a function can be entered that defines the way of how treatment arms
are selected. This function is allowed to depend on effectVector with length activeArms,
stage, "conditionalPower", "conditionalCriticalValue", "plannedSubjects/plannedEvents",
"allocationRatioPlanned", "selectedArms", "thetaH1" (for means and survival), "stDevH1" (for means),
"overallEffects", and for rates additionally: "piTreatmentsH1", "piControlH1", "overallRates", and
"overallRatesControl".
Logical. If TRUE, summary statistics of the simulated data
are displayed for the print command, otherwise the output is suppressed, default
is FALSE.
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.
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 thetaH1 makes only sense if kMax > 1
and if conditionalPower, minNumberOfEventsPerStage, and
maxNumberOfEventsPerStage (or calcEventsFunction) are defined.
calcEventsFunction
This function returns the number of events at given conditional power
and conditional critical value for specified testing situation.
The function might depend on the variables
stage,
selectedArms,
plannedEvents,
directionUpper,
allocationRatioPlanned,
minNumberOfEventsPerStage,
maxNumberOfEventsPerStage,
conditionalPower,
conditionalCriticalValue, and
overallEffects.
The function has to contain the three-dots argument '...' (see examples).
if (FALSE) {
# Assess different selection rules for a two-stage survival design with
# O'Brien & Fleming alpha spending boundaries and (non-binding) stopping
# for futility if the test statistic is negative.
# Number of events at the second stage is adjusted based on conditional
# power 80% and specified minimum and maximum number of Events.
design <- getDesignInverseNormal(typeOfDesign = "asOF", futilityBounds = 0)
y1 <- getSimulationMultiArmSurvival(design = design, activeArms = 4,
intersectionTest = "Simes", typeOfShape = "sigmoidEmax",
omegaMaxVector = seq(1, 2, 0.5), gED50 = 2, slope = 4,
typeOfSelection = "best", conditionalPower = 0.8,
minNumberOfEventsPerStage = c(NA_real_, 30),
maxNumberOfEventsPerStage = c(NA_real_, 90),
maxNumberOfIterations = 50,
plannedEvents = c(75, 120))
y2 <- getSimulationMultiArmSurvival(design = design, activeArms = 4,
intersectionTest = "Simes", typeOfShape = "sigmoidEmax",
omegaMaxVector = seq(1,2,0.5), gED50 = 2, slope = 4,
typeOfSelection = "epsilon", epsilonValue = 0.2,
effectMeasure = "effectEstimate",
conditionalPower = 0.8, minNumberOfEventsPerStage = c(NA_real_, 30),
maxNumberOfEventsPerStage = c(NA_real_, 90),
maxNumberOfIterations = 50,
plannedEvents = c(75, 120))
y1$effectMatrix
y1$rejectAtLeastOne
y2$rejectAtLeastOne
y1$selectedArms
y2$selectedArms
}
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