# ASSISTDesignC: A fixed sample RCT design to compare against the adaptive... In ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials

 ASSISTDesignC 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.

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

##### 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.

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.