ASSISTDesignC {ASSISTant} | R Documentation |
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
Inherited methods
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"))