Description Usage Format Methods References See Also Examples
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.
1 | # design <- ASSISTDesignC$new(trialParameters, designParameters)
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An R6Class
generator object
ASSISTDesignC$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$getDesignParameters()
as would be the case for a Type I error calculation. If changed, would yield power. Also showProgress = TRUE/FALSE
, saveRawData = TRUE/FALSE
control raw data saves and display of progress. Returns a list of results
analyze(trialExploration)
Analyze
the design given the trialExploration
which is the result of a call to explore
to
simulate the design. Return a list of summary quantities
summary(analysis)
Print the operating characteristics of the design, using the analysis
result from the analyze
call
Adaptive Choice of Patient Subgroup for Comparing Two Treatments by Tze Leung Lai and Philip W. Lavori and Olivia Yueh-Wen Liao. Contemporary Clinical Trials, Vol. 39, No. 2, pp 191-200 (2014). doi:10.1016/j.cct.2014.09.001g
ASSISTDesignB
which is a superclass of this object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | data(LLL.SETTINGS)
prevalence <- LLL.SETTINGS$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"))
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