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Adaptive Subgroup Selection in Sequential Trials
In ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials
title: "Adaptive Subgroup Selection in Sequential Trials"
author: "Tze Leung Lai, Philip W. Lavori, Olivia Liao, Ka Wai Tsang
and Balasubramanian Narasimhan"
date: 'r Sys.Date()'
bibliography: assistant.bib
output:
html_document:
theme: cerulean
toc: yes
toc_depth: 2
vignette: >
%\VignetteIndexEntry{Adaptive Subgroup Selection in Sequential Trials}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
### get knitr just the way we like it
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
error = FALSE,
tidy = FALSE,
cache = FALSE
)
Introduction
ASSISTant is an R package for Adaptive Subgroup
Selection In Sequential Trials. This vignette
reproduces all the simulations in the original paper of Lai, Lavori
and Liao [-@Lai2014191].
library(ASSISTant)
data(LLL.SETTINGS)
str(LLL.SETTINGS)
The LLL.SETTINGS list contains all the scenarios used for the null
and alternative cases in Lai, Lavori and Liao [-@Lai2014191].
Table 1 Results
The results shown here should closely approximate those in Table 1 of
Lai, Lavori and Liao [-@Lai2014191].
Scenario S0
This is the null setting.
scenario <- LLL.SETTINGS$scenarios$S0
designParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
designA <- ASSISTDesign$new(trialParameters = LLL.SETTINGS$trialParameters,
designParameters = designParameters)
print(designA)
result <- designA$explore(numberOfSimulations = 25000, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S1
scenario <- LLL.SETTINGS$scenarios$S1
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S2
scenario <- LLL.SETTINGS$scenarios$S2
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S3
scenario <- LLL.SETTINGS$scenarios$S3
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S4
scenario <- LLL.SETTINGS$scenarios$S4
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S5
scenario <- LLL.SETTINGS$scenarios$S5
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S6
scenario <- LLL.SETTINGS$scenarios$S6
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S7
scenario <- LLL.SETTINGS$scenarios$S7
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S8
scenario <- LLL.SETTINGS$scenarios$S8
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S9
scenario <- LLL.SETTINGS$scenarios$S9
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S10
scenario <- LLL.SETTINGS$scenarios$S10
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000,
trueParameters = trueParameters, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Table 2 Results
Scenario S0
scenario <- LLL.SETTINGS$scenarios$S0
designParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
designA <- ASSISTDesign$new(trialParameters = LLL.SETTINGS$trialParameters,
designParameters = designParameters)
print(designA)
result <- designA$explore(numberOfSimulations = 25000, showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S1
scenario <- LLL.SETTINGS$scenarios$S1
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S2
scenario <- LLL.SETTINGS$scenarios$S2
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S3
scenario <- LLL.SETTINGS$scenarios$S3
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S4
scenario <- LLL.SETTINGS$scenarios$S4
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S5
scenario <- LLL.SETTINGS$scenarios$S5
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S6
scenario <- LLL.SETTINGS$scenarios$S6
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S7
scenario <- LLL.SETTINGS$scenarios$S7
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S8
scenario <- LLL.SETTINGS$scenarios$S8
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S9
scenario <- LLL.SETTINGS$scenarios$S9
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
Alternative Scenario S10
scenario <- LLL.SETTINGS$scenarios$S10
trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2,
mean = scenario$mean,
sd = scenario$sd)
result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters,
showProgress = FALSE)
analysis <- designA$analyze(result)
print(designA$summary(analysis))
References
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ASSISTant documentation built on Dec. 2, 2022, 5:12 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Adaptive Subgroup Selection in Sequential Trials"
author: "Tze Leung Lai, Philip W. Lavori, Olivia Liao, Ka Wai Tsang
and Balasubramanian Narasimhan"
date: 'r Sys.Date()'
bibliography: assistant.bib
output:
html_document:
theme: cerulean
toc: yes
toc_depth: 2
vignette: >
%\VignetteIndexEntry{Adaptive Subgroup Selection in Sequential Trials}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
### get knitr just the way we like it knitr::opts_chunk$set( message = FALSE, warning = FALSE, error = FALSE, tidy = FALSE, cache = FALSE )
Introduction
ASSISTant is an R package for Adaptive Subgroup
Selection In Sequential Trials. This vignette
reproduces all the simulations in the original paper of Lai, Lavori
and Liao [-@Lai2014191].
library(ASSISTant) data(LLL.SETTINGS) str(LLL.SETTINGS)
The LLL.SETTINGS list contains all the scenarios used for the null
and alternative cases in Lai, Lavori and Liao [-@Lai2014191].
Table 1 Results
The results shown here should closely approximate those in Table 1 of Lai, Lavori and Liao [-@Lai2014191].
Scenario S0
This is the null setting.
scenario <- LLL.SETTINGS$scenarios$S0 designParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) designA <- ASSISTDesign$new(trialParameters = LLL.SETTINGS$trialParameters, designParameters = designParameters) print(designA)
result <- designA$explore(numberOfSimulations = 25000, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S1
scenario <- LLL.SETTINGS$scenarios$S1 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S2
scenario <- LLL.SETTINGS$scenarios$S2 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S3
scenario <- LLL.SETTINGS$scenarios$S3 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S4
scenario <- LLL.SETTINGS$scenarios$S4 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S5
scenario <- LLL.SETTINGS$scenarios$S5 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S6
scenario <- LLL.SETTINGS$scenarios$S6 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S7
scenario <- LLL.SETTINGS$scenarios$S7 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S8
scenario <- LLL.SETTINGS$scenarios$S8 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S9
scenario <- LLL.SETTINGS$scenarios$S9 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S10
scenario <- LLL.SETTINGS$scenarios$S10 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table1, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Table 2 Results
Scenario S0
scenario <- LLL.SETTINGS$scenarios$S0 designParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) designA <- ASSISTDesign$new(trialParameters = LLL.SETTINGS$trialParameters, designParameters = designParameters) print(designA)
result <- designA$explore(numberOfSimulations = 25000, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S1
scenario <- LLL.SETTINGS$scenarios$S1 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S2
scenario <- LLL.SETTINGS$scenarios$S2 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S3
scenario <- LLL.SETTINGS$scenarios$S3 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S4
scenario <- LLL.SETTINGS$scenarios$S4 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S5
scenario <- LLL.SETTINGS$scenarios$S5 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S6
scenario <- LLL.SETTINGS$scenarios$S6 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S7
scenario <- LLL.SETTINGS$scenarios$S7 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S8
scenario <- LLL.SETTINGS$scenarios$S8 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S9
scenario <- LLL.SETTINGS$scenarios$S9 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
Alternative Scenario S10
scenario <- LLL.SETTINGS$scenarios$S10 trueParameters <- list(prevalence = LLL.SETTINGS$prevalences$table2, mean = scenario$mean, sd = scenario$sd) result <- designA$explore(numberOfSimulations = 25000, trueParameters = trueParameters, showProgress = FALSE) analysis <- designA$analyze(result) print(designA$summary(analysis))
References
Try the ASSISTant package in your browser
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