ASSISTDesignC: A fixed sample RCT design to compare against the adaptive...

In ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials

A fixed sample RCT design to compare against the adaptive clinical trial design of Lai, Lavori and Liao.

Description

ASSISTDesignC objects are used to design a trial with certain characteristics provided in the object instantiation method. This design differs from ASSISTDesign in only how it computes the critical boundaries, how it performs the interim look, and what quantities are computed in a trial run.

Super classes

ASSISTant::ASSISTDesign -> ASSISTant::ASSISTDesignB -> ASSISTDesignC

Methods

Public methods

• ASSISTDesignC$computeCriticalValues()

• ASSISTDesignC$explore()

• ASSISTDesignC$analyze()

• ASSISTDesignC$summary()

• ASSISTDesignC$clone()

Inherited methods

• ASSISTant::ASSISTDesign$getBoundaries()
• ASSISTant::ASSISTDesign$getDesignParameters()
• ASSISTant::ASSISTDesign$getTrialParameters()
• ASSISTant::ASSISTDesign$initialize()
• ASSISTant::ASSISTDesign$performInterimLook()
• ASSISTant::ASSISTDesign$print()
• ASSISTant::ASSISTDesign$setBoundaries()

---

Method computeCriticalValues()

Compute the critical boundary values \tilde{b}, b and c for futility, efficacy and final efficacy decisions. This is time consuming so cache where possible.

Usage

ASSISTDesignC$computeCriticalValues()

Returns

a named list containing the critical value cAlpha

---

Method explore()

Explore the design using the specified number of simulations and random number seed and other parameters.

Usage

ASSISTDesignC$explore(
  numberOfSimulations = 5000,
  rngSeed = 12345,
  trueParameters = self$getDesignParameters(),
  showProgress = TRUE,
  saveRawData = FALSE
)

Arguments

numberOfSimulations

default number of simulations is 5000

rngSeed

default seed is 12345

trueParameters

the state of nature, by default the value of self$getDesignParameters() as would be the case for a Type I error calculation. If changed, would yield power.

showProgress

a boolean flag to show progress, default TRUE

saveRawData

a flag (default FALSE) to indicate if raw data has to be saved

Returns

a list of results

---

Method analyze()

Analyze the design given the trialExploration data

Usage

ASSISTDesignC$analyze(trialExploration)

Arguments

trialExploration

the results from a call to explore() to simulate the design

Returns

a named list of rejections

---

Method summary()

Print the operating characteristics of the design using the analysis data

Usage

ASSISTDesignC$summary(analysis)

Arguments

analysis

the analysis result from the analyze() call

Returns

no value, just print

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ASSISTDesignC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

ASSISTDesignB which is a superclass of this object

Examples

data(LLL.SETTINGS)
prevalence <- LLL.SETTINGS$prevalences$table1
scenario <- LLL.SETTINGS$scenarios$S0
designParameters <- list(prevalence = prevalence,
                       mean = scenario$mean,
                       sd = scenario$sd)
## A realistic design uses 5000 simulations or more!
designC <- ASSISTDesignC$new(trialParameters = LLL.SETTINGS$trialParameters,
                            designParameters = designParameters)
print(designC)
result <- designC$explore(numberOfSimulations = 100, showProgress = interactive())
analysis <- designC$analyze(result)
designC$summary(analysis)
## For full examples, try:
## browseURL(system.file("full_doc/ASSISTant.html", package="ASSISTant"))

ASSISTant documentation built on Dec. 2, 2022, 5:12 p.m.