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

In bnaras/ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials

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.

Usage

# design <- ASSISTDesignC$new(trialParameters, designParameters)

Format

An R6Class generator object

Methods

ASSISTDesignC$new(designParameters, trialParameters, discreteData = FALSE, boundaries)

Create a new ASSISTDesign instance object using the parameters specified. If discreteData is TRUE use a discrete distribution for the Rankin scores and designParameters must contain the appropriate distributions to sample from. If 'boundaries is specified, it is used.

getDesignameters,getTrialParameters, getBoundaries

Accessor methods for (obvious) object slots

setBoundaries

Modifier method for boundaries a named vector of double values with names btilde, b, and c, in that order

print()

Print the object in a human readable form

computeCriticalValues()

Compute the critical boundary value c_α

explore(numberOfSimulations = 5000, rngSeed = 12345

Explore the design using the specified number of simulations and random number seed. There are further parameters. By default trueParameters = self$getDesignParameters() as would be the case for a Type I error calculation. If changed, would yield power. Also showProgress = TRUE/FALSE, saveRawData = TRUE/FALSE control raw data saves and display of progress. Returns a list of results

analyze(trialExploration)

Analyze the design given the trialExploration which is the result of a call to explore to simulate the design. Return a list of summary quantities

summary(analysis)

Print the operating characteristics of the design, using the analysis result from the analyze call

References

Adaptive Choice of Patient Subgroup for Comparing Two Treatments by Tze Leung Lai and Philip W. Lavori and Olivia Yueh-Wen Liao. Contemporary Clinical Trials, Vol. 39, No. 2, pp 191-200 (2014). doi:10.1016/j.cct.2014.09.001g

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"))

bnaras/ASSISTant documentation built on Nov. 23, 2019, 6:20 p.m.