Using Discrete Rankin Scores

In ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials

### get knitr just the way we like it

knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  error = FALSE,
  tidy = FALSE,
  cache = FALSE
)

Introduction

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)

Scenario S0

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))

ASSISTant documentation built on Dec. 2, 2022, 5:12 p.m.