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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)## Design Parameters:
## Number of Groups: 6
## Prevalence:
##
## | Group1| Group2| Group3| Group4| Group5| Group6|
## |---------:|---------:|---------:|---------:|---------:|---------:|
## | 0.1666667| 0.1666667| 0.1666667| 0.1666667| 0.1666667| 0.1666667|
##
## Using Discrete Rankin scores? TRUE
##
## Null Rankin Distribution:
##
## | | Group1| Group2| Group3| Group4| Group5| Group6|
## |:--|---------:|---------:|---------:|---------:|---------:|---------:|
## |0 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## |1 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## |2 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## |3 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## |4 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## |5 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## |6 | 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571| 0.1428571|
## Null Mean and SD
##
## | | Group1| Group2| Group3| Group4| Group5| Group6|
## |:----|------:|------:|------:|------:|------:|------:|
## |mean | 3| 3| 3| 3| 3| 3|
## |sd | 2| 2| 2| 2| 2| 2|
## Alternative Rankin Distribution:
##
##
## | | Group1| Group2| Group3| Group4| Group5| Group6|
## |:--|---------:|---------:|---------:|---------:|---------:|---------:|
## |0 | 0.1428571| 0.1428571| 0.1428571| 0.0909091| 0.0909091| 0.0909091|
## |1 | 0.1428571| 0.1428571| 0.1428571| 0.1818182| 0.1818182| 0.1818182|
## |2 | 0.1428571| 0.1428571| 0.1428571| 0.1818182| 0.1818182| 0.1818182|
## |3 | 0.1428571| 0.1428571| 0.1428571| 0.0909091| 0.0909091| 0.0909091|
## |4 | 0.1428571| 0.1428571| 0.1428571| 0.1818182| 0.1818182| 0.1818182|
## |5 | 0.1428571| 0.1428571| 0.1428571| 0.1818182| 0.1818182| 0.1818182|
## |6 | 0.1428571| 0.1428571| 0.1428571| 0.0909091| 0.0909091| 0.0909091|
## Alternative Mean and SD
##
## | | Group1| Group2| Group3| Group4| Group5| Group6|
## |:----|------:|------:|------:|--------:|--------:|--------:|
## |mean | 3| 3| 3| 3.000000| 3.000000| 3.000000|
## |sd | 2| 2| 2| 1.858641| 1.858641| 1.858641|
##
## Trial Parameters:
## List of 5
## $ N : num [1:3] 300 400 500
## $ type1Error: num 0.05
## $ eps : num 0.5
## $ type2Error: num 0.2
## $ effectSize: num 0.0642
##
## Boundaries:
##
##
## | btilde| b| c|
## |---------:|--------:|--------:|
## | -1.460993| 2.390404| 2.491775|result <- designA$explore(numberOfSimulations = 5000, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))## P(Reject H0_ITT) = 0.017200; P(Reject H0_subgp) = 0.021800; P(Reject H0) = 0.039000
## P(Early stop for efficacy [futility]) = 0.024400 [0.568800]
## Mean [SD] Randomized N = 419.620000 [76.093453]
##
## Stage at exit (proportion)
##
##
## | exitStage| proportion|
## |---------:|----------:|
## | 1| 0.2106|
## | 2| 0.3826|
## | 3| 0.4068|
##
## Mean [SD] Lost N = 183.574400 [92.930368]
## Mean [SD] Analyzed N = 236.045600 [97.620219]
##
## Mean loss by futility stage and subgroup
##
##
## | FutilityStage| selectedGroup| mean| sd|
## |-------------:|-------------:|---------:|---------:|
## | 1| 1| 250.28419| 6.348342|
## | 1| 2| 200.61538| 8.317006|
## | 1| 3| 150.09895| 8.528229|
## | 1| 4| 100.22222| 8.528198|
## | 1| 5| 50.00383| 6.256512|
## | 2| 1| 332.21970| 6.692042|
## | 2| 2| 266.84762| 9.663354|
## | 2| 3| 201.28723| 10.548019|
## | 2| 4| 132.33628| 9.354726|
## | 2| 5| 66.10778| 7.323037|
## | 3| 1| 416.92357| 8.126832|
## | 3| 2| 332.46825| 9.598072|
## | 3| 3| 249.78462| 10.513177|
## | 3| 4| 166.63415| 10.659841|
## | 3| 5| 82.41924| 8.427552|
##
## Chance of each subpopulation rejected
##
##
## | group| count| proportion|
## |-----:|-----:|----------:|
## | 1| 40| 0.0080|
## | 2| 30| 0.0060|
## | 3| 18| 0.0036|
## | 4| 13| 0.0026|
## | 5| 8| 0.0016|
## | 6| 86| 0.0172|
##
## Counts by futility stage and subgroup choice
##
##
## | FutilityStage| G1| G2| G3| G4| G5|
## |-------------:|----:|---:|---:|---:|---:|
## | 1| 1309| 715| 475| 414| 522|
## | 2| 132| 105| 94| 113| 167|
## | 3| 157| 126| 130| 164| 291|
##
## CI Statistics:
## Overall coverage and coverage for rejections:
##
## | overall| rejection|
## |-------:|---------:|
## | 1| 1|
##
## P(theta_test is in the confidence interval)
##
##
## | coverage| selectedCount| rejectedCount|
## |--------:|-------------:|-------------:|
## | 1| 1598| 40|
## | 1| 946| 30|
## | 1| 699| 18|
## | 1| 691| 13|
## | 1| 980| 8|
## | 1| 86| 86|
## NULLThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.