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ASSISTant

ASSISTant is a package to assist in performing Adaptive Subgroup Selection In Sequential Trials.

ASSISTant provides a three-stage adaptive group sequential clinical trial design with provision for selecting a subgroup where the treatment may be effective. The package provides facilities for design, exploration and analysis of such trials using either continuous or discrete outcomes. A complete implementation of the initial design for the DEFUSE-3 trial is also provided as a vignette.

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install.packages('ASSISTant')

Monthly Downloads

318

Version

1.4.3

License

GPL (>= 2)

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Last Published

December 2nd, 2022

Functions in ASSISTant (1.4.3)

scalarInRange

Is a scalar quantity is a specified range?
ASSISTDesignC

A fixed sample RCT design to compare against the adaptive clinical trial design of Lai, Lavori and Liao.
LLL.SETTINGS

Design and trial settings used in the Lai, Lavori, Liao paper simulations
colNamesForStage

Return a vector of column names for statistics for a given stage
ASSISTant

Three stage group sequential adaptive design with subgroup selection
computeMHPBoundaries

Compute the three modified Haybittle-Peto boundaries
computeMHPBoundaryITT

Compute the three modified Haybittle-Peto boundaries and effect size
DEFUSE3Design

The DEFUSE3 design
wilcoxon

Compute the standardized Wilcoxon test statistic for two samples
generateDiscreteData

A data generation function using a discrete distribution for Rankin score rather than a normal distribution
generateNormalData

A data generation function along the lines of what was used in the Lai, Lavori, Liao paper. score rather than a normal distribution
ASSISTDesign

A class to encapsulate the adaptive clinical trial design of Lai, Lavori and Liao
groupSampleSize

Compute the sample size for any group at a stage assuming a nested structure as in the paper.
mHP.b

Compute the efficacy boundary (modified Haybittle-Peto) for the first two stages
ASSISTDesignB

A fixed sample design to compare against the adaptive clinical trial design
mHP.c

Compute the efficacy boundary (modified Haybittle-Peto) for the final (third) stage
mHP.btilde

Compute the futility boundary (modified Haybittle-Peto) for the first two stages
computeMeanAndSD

Compute the mean and sd of a discrete Rankin distribution
conformParameters

Conform designParameters so that weights are turned in to probabilities, the null and control distributions are proper matrices etc.