DEFUSE3Design: The DEFUSE3 design
In bnaras/ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials

Description Usage Format Methods References See Also Examples

DEFUSE3Design is a slight variant of the the adaptive clinical trial design of Lai, Lavori and Liao. Simulation is used to compute the expected maximum sample size and the boundary for early futility is adjusted to account as well.

1	# design <- DEFUSE3Design$new(designParameters, trialParameters)

An R6Class generator object

DEFUSE3Design$new(designParameters, trialParameters, discreteData = FALSE, numberOfSimulations = 5000, rngSeed = 54321, showProgress = TRUE, boundaries): Create a new DEFUSE3Design instance object using the parameters specified. If discreteData is TRUE use a discrete distribution for the Rankin scores and designParameters must contain the appropriate distributions to sample from. If boundaries is specified, it is used.
getDesignParameters,getTrialParameters, getBoundaries: Accessor methods for (obvious) object slots
setBoundaries: Modifier method for boundaries a named vector of double values with names btilde, b, and c, in that order
print(): Print the object in a human readable form
adjustCriticalValues(numberOfSimulations, rngSeed, showProgress): Adjust the critical values by performing simulations using the parameters provided
computeCriticalValues(): Compute the critical boundary value c_α
explore(numberOfSimulations = 5000, rngSeed = 12345, trueParameters = self$getDesignParameters(), recordStats = TRUE, showProgress = TRUE, saveRawData = FALSE): Explore the design using the specified number of simulations and random number seed. trueParameters is by default the same as designParameters as would be the case for a Type I error calculation. If changed, would yield power. Record statistics, save raw data and show progress if so desired. Returns a list of results
analyze(trialHistory): Analyze the design given the trialHistory 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 design of confirmatory trials: Advances and challenges, 2015 45(Pt A):93-102, by Tze Leung Lai and Philip W. Lavori and Ka Wai Tsang. doi:10.1016/j.cct.2015.06.007

ASSISTDesign which is a superclass of this object

trialParameters <- list(N = c(200, 340, 476), type1Error = 0.025,
                        eps = 1/2, type2Error = 0.1)
designParameters <- list(
   nul0 = list(prevalence = rep(1/6, 6), mean = matrix(0, 2, 6),
               sd = matrix(1, 2, 6)),
   alt1 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
               c(0.5, 0.4, 0.3, 0, 0, 0)),
               sd = matrix(1, 2, 6)),
   alt2 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
               c(0.5, 0.5, 0, 0, 0, 0)),
               sd = matrix(1,2, 6)),
   alt3 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6), rep(0.36, 6)),
               sd = matrix(1,2, 6)),
   alt4 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6), rep(0.30, 6)),
               sd = matrix(1,2, 6)),
   alt5 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
               c(0.4, 0.3, 0.2, 0, 0, 0)),
               sd = matrix(1,2, 6)),
   alt6 = list(prevalence = rep(1/6, 6), mean = rbind(rep(0, 6),
               c(0.5, 0.5, 0.3, 0.3, 0.1, 0.1)),
               sd = matrix(1,2, 6)))

## Not run: 
## A realistic design uses 5000 simulations or more!
defuse3 <- DEFUSE3Design$new(trialParameters = trialParameters,
                             numberOfSimulations = 25,
                             designParameters = designParameters$nul0,
                             showProgress = FALSE)
print(defuse3)
result <- defuse3$explore(showProgress = interactive())
analysis <- defuse3$analyze(result)
print(defuse3$summary(analysis))

## End(Not run)
## For full examples, try:
## browseURL(system.file("full_doc/defuse3.html", package="ASSISTant"))