ASSISTDesign: A class to encapsulate the adaptive clinical trial design of...

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

Description

ASSISTDesign objects are used to design, simulate and analyze adaptive group sequential clinical trial with three stages.

Usage

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

Format

An R6Class generator object

Methods

ASSISTDesign$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 used.

getDesignParameters,getTrialParameters, getBoundaries

Accessor methods for (obvious) object fields

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 values \tilde{b}, b and c for futility, efficacy and final efficacy decisions; saved in field boundaries

explore(numberOfSimulations = 5000, rngSeed = 12345)

Explore the design using the specified number of simulations and random number seed. There are a number of further parameters. By default trueParameters = self$getDesignParameters() as would be the case for a Type I error calculation. If changed, would yield power. Also recordStats = TRUE/FALSE, showProgress = TRUE/FALSE, saveRawData = TRUE/FALSE control recording statistics, raw data saves, display of progress. Fixed sample size (fixedSampleSize = TRUE/FALSE) can be specified to ensure that patients lost after a futile overall look are not made up. 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

LLL.SETTINGS for an explanation of trial parameters

Examples

## Not run: 
data(LLL.SETTINGS)
prevalence <- LLL.SETTINGS$prevalences$table1
scenario <- LLL.SETTINGS$scenarios$S0
designParameters <- list(prevalence = prevalence,
                       mean = scenario$mean,
                       sd = scenario$sd)
designA <- ASSISTDesign$new(trialParameters = LLL.SETTINGS$trialParameters,
                            designParameters = designParameters)
print(designA)
## A realistic design uses 5000 simulations or more!
result <- designA$explore(showProgress = interactive())
analysis <- designA$analyze(result)
designA$summary(analysis)

## End(Not run)
## 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.