# 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))
```

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