ASSISTDesignC | R Documentation |

`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.

`ASSISTant::ASSISTDesign`

-> `ASSISTant::ASSISTDesignB`

-> `ASSISTDesignC`

`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()

a named list containing the critical value `cAlpha`

`explore()`

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

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

`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

a list of results

`analyze()`

Analyze the design given the `trialExploration`

data

ASSISTDesignC$analyze(trialExploration)

`trialExploration`

the results from a call to

`explore()`

to simulate the design

a named list of rejections

`summary()`

Print the operating characteristics of the design using the analysis data

ASSISTDesignC$summary(analysis)

`analysis`

the analysis result from the

`analyze()`

call

no value, just print

`clone()`

The objects of this class are cloneable with this method.

ASSISTDesignC$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

`ASSISTDesignB`

which is a superclass of this object

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"))

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.