Package: ASSISTant 1.4.3

Balasubramanian Narasimhan

ASSISTant: Adaptive Subgroup Selection in Group Sequential Trials

Clinical trial design for subgroup selection in three-stage group sequential trial. Includes facilities for design, exploration and analysis of such trials. An implementation of the initial DEFUSE-3 trial is also provided as a vignette.

Authors:Tze Leung Lai [ctb], Philip Lavori [aut], Olivia Liao [aut], Balasubramanian Narasimhan [aut, cre], Ka Wai Tsang [aut]

ASSISTant_1.4.3.tar.gz
ASSISTant_1.4.3.zip(r-4.7-any)ASSISTant_1.4.3.zip(r-4.6-any)ASSISTant_1.4.3.zip(r-4.5-any)
ASSISTant_1.4.3.tgz(r-4.6-any)ASSISTant_1.4.3.tgz(r-4.5-any)
ASSISTant_1.4.3.tar.gz(r-4.7-any)ASSISTant_1.4.3.tar.gz(r-4.6-any)
ASSISTant_1.4.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ASSISTant/json (API)

# Install 'ASSISTant' in R:
install.packages('ASSISTant', repos = c('https://bnaras.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bnaras/assistant/issues

Datasets:
  • LLL.SETTINGS - Design and trial settings used in the Lai, Lavori, Liao paper simulations

On CRAN:

Conda:

4.64 score 29 scripts 400 downloads 15 exports 21 dependencies

Last updated from:b6598c72ff. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develERROR109
source / vignettesOK341
linux-releaseERROR153
macos-releaseERROR115
macos-oldrelERROR120
windows-develERROR80
windows-releaseERROR88
windows-oldrelERROR80
wasm-releaseOK87

Exports:ASSISTDesignASSISTDesignBASSISTDesignCcolNamesForStagecomputeMeanAndSDcomputeMHPBoundariescomputeMHPBoundaryITTDEFUSE3DesigngenerateDiscreteDatagenerateNormalDatagroupSampleSizemHP.bmHP.btildemHP.cwilcoxon

Dependencies:clidplyrevaluategenericsgluehighrknitrlifecyclemagrittrmvtnormpillarpkgconfigR6rlangtibbletidyselectutf8vctrswithrxfunyaml

Adaptive Subgroup Selection in Sequential Trials
Introduction | Table 1 Results | Scenario S0 | Alternative Scenario S1 | Alternative Scenario S2 | Alternative Scenario S3 | Alternative Scenario S4 | Alternative Scenario S5 | Alternative Scenario S6 | Alternative Scenario S7 | Alternative Scenario S8 | Alternative Scenario S9 | Alternative Scenario S10 | Table 2 Results | References
Rendered fromASSISTant.Rmdusingknitr::rmarkdown

Last update: 2019-11-22
Started: 2016-05-02

Design of the DEFUSE3 Trial
Continuous Rankin Simulations | The NULL Scenario | The ALT1 Scenario | The ALT2 Scenario | Discrete Rankin Simulations | The Discretized Scenarios | References
Rendered fromdefuse3.Rmdusingknitr::rmarkdown

Last update: 2019-11-22
Started: 2016-05-02

Using Discrete Rankin Scores
Introduction | Scenario S0
Rendered fromRankin.Rmdusingknitr::rmarkdown

Last update: 2019-11-22
Started: 2017-06-30

Readme and manuals

Help Manual

Help pageTopics
Three stage group sequential adaptive design with subgroup selectionASSISTant-package ASSISTant
A class to encapsulate the adaptive clinical trial design of Lai, Lavori and LiaoASSISTDesign
A fixed sample design to compare against the adaptive clinical trial design of Lai, Lavori and Liao.ASSISTDesignB
A fixed sample RCT design to compare against the adaptive clinical trial design of Lai, Lavori and Liao.ASSISTDesignC
Return a vector of column names for statistics for a given stagecolNamesForStage
Compute the mean and sd of a discrete Rankin distributioncomputeMeanAndSD
Compute the three modified Haybittle-Peto boundariescomputeMHPBoundaries
Compute the three modified Haybittle-Peto boundaries and effect sizecomputeMHPBoundaryITT
The DEFUSE3 designDEFUSE3Design
A data generation function using a discrete distribution for Rankin score rather than a normal distributiongenerateDiscreteData
A data generation function along the lines of what was used in the Lai, Lavori, Liao paper. score rather than a normal distributiongenerateNormalData
Compute the sample size for any group at a stage assuming a nested structure as in the paper.groupSampleSize
Design and trial settings used in the Lai, Lavori, Liao paper simulationsLLL.SETTINGS
Compute the efficacy boundary (modified Haybittle-Peto) for the first two stagesmHP.b
Compute the futility boundary (modified Haybittle-Peto) for the first two stagesmHP.btilde
Compute the efficacy boundary (modified Haybittle-Peto) for the final (third) stagemHP.c
Compute the standardized Wilcoxon test statistic for two sampleswilcoxon