knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Confirmatory Adaptive Clinical Trial Design, Simulation, and Analysis.
We recommend three ways to learn how to use rpact
:
- Use the Shiny app: shiny.rpact.com
- Use the Vignettes: www.rpact.org/vignettes
- Book a training: www.rpact.com
The vignettes are hosted at www.rpact.org/vignettes and cover the following topics:
design <- getDesignGroupSequential()
rpact
comparison tools:
getDesignSet
getSampleSizeMeans()
,
getPowerMeans()
getSimulationMeans()
data <- getDataset()
getAnalysisResults(design, data)
The most important rpact
functions have intuitive names:
getDesign
[GroupSequential
/InverseNormal
/Fisher
]()
getDesignCharacteristics()
getSampleSize
[Means
/Rates
/Survival
]()
getPower
[Means
/Rates
/Survival
]()
getSimulation
[MultiArm
/Enrichment
]`[
Means/
Rates/
Survival]
()`getDataSet()
getAnalysisResults()
getStageResults()
RStudio/Eclipse: auto code completion makes it easy to use these functions.
In general, everything runs with the R standard functions which are always
present in R: so-called R generics, e.g., print
, summary
, plot
,
as.data.frame
, names
, length
Several utility functions are available, e.g.
getAccrualTime()
getPiecewiseSurvivalTime()
getNumberOfSubjects()
getEventProbabilities()
getPiecewiseExponentialDistribution()
pi
, lambda
and median
, e.g.,
getLambdaByMedian()
testPackage()
: installation qualification on a client computer or company
server (via unit tests)Please contact us to learn how to use rpact
on FDA/GxP-compliant validated corporate computer systems and how to get a copy
of the formal validation documentation that is customized and licensed for
exclusive use by your company, e.g., to fulfill regulatory requirements.
For more information please visit www.rpact.org
rpact
packageFor more information please visit www.rpact.com
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