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### get knitr just the way we like it knitr::opts_chunk$set( message = FALSE, warning = FALSE, error = FALSE, tidy = FALSE, cache = FALSE )

We simulate data from a discrete distribution for the Rankin scores, which are ordinal integers from 0 to 6 in the following simulations. So we define a few scenarios.

library(ASSISTant) null.uniform <- rep(1, 7L) ## uniform on 7 support points hourglass <- c(1, 2, 2, 1, 2, 2, 1) inverted.hourglass <- c(2, 1, 1, 2, 1, 1, 2) bottom.heavy <- c(2, 2, 2, 1, 1, 1, 1) bottom.heavier <- c(3, 3, 2, 2, 1, 1, 1) top.heavy <- c(1, 1, 1, 1, 2, 2, 2) top.heavier <- c(1, 1, 1, 2, 2, 3, 3)

ctlDist <- null.uniform trtDist <- cbind(null.uniform, null.uniform, null.uniform, hourglass, hourglass, hourglass) ##d <- generateDiscreteRankinScores(rep(1, 6), 10, ctlDist, trtDist)

This is the *null* setting.

data(LLL.SETTINGS) designParameters <- list(prevalence = rep(1/6, 6), ctlDist = ctlDist, trtDist = trtDist) designA <- ASSISTDesign$new(trialParameters = LLL.SETTINGS$trialParameters, designParameters = designParameters, discreteData = TRUE) print(designA)

result <- designA$explore(numberOfSimulations = 5000, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))

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