rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

rpact (R Package for Adaptive Clinical Trials) is a R package that enables the design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoint.

Installation

Installation from CRAN

# The easiest way to get rpact is to install it from cran: 
install.packages("rpact")

Installation of the latest developer version

Please follow the instructions described here.

Basic Functions and Classes

Design Functions

  • getDesignGroupSequential()
  • getDesignInverseNormal()
  • getDesignFisher()
  • getDesignCharacteristics()

Sample Size Calculation Functions

  • getSampleSizeMeans()
  • getSampleSizeRates()
  • getSampleSizeSurvival()

Power Calculation Functions

  • getPowerMeans()
  • getPowerRates()
  • getPowerSurvival()

Simulation Functions

  • getSimulationMeans()
  • getSimulationRates()
  • getSimulationSurvival()
  • getSimulationMultiArmMeans()
  • getSimulationMultiArmRates()
  • getSimulationMultiArmSurvival()

Dataset and Analysis Results Functions

  • getDataset()
  • getAnalysisResults()
  • getStageResults()

Plot Functions

Take a look at the appearance and graphical output of the rpact package

Usage

Getting started

# load the package
library(rpact)
 
# display the manual of the package
help(package = "rpact")
 
# create an inverse normal design with default parameters
design <- getDesignInverseNormal()
 
# take a look at the design and its default values
design 
 
# display the design characteristics
getDesignCharacteristics(design)
 
# plot the design with default type 1 (Boundary Plot)
plot(design)
 
# create an 'Average Sample Size and Power / Early Stop' plot
plot(design, type = 2, nMax = 12)

Working with datasets

# create a group sequential design
design <- getDesignGroupSequential(kMax = 4, alpha = 0.025,
    informationRates = c(0.2, 0.5, 0.8, 1),
    futilityBounds = rep(0.5244, 4 - 1),
    typeOfDesign = "WT", deltaWT = 0.45)
 
# take a look at the design
design 
 
# create a dataset of means
data <- getDataset(
    n1 = c(22, 11, 22, 11),
    n2 = c(22, 13, 22, 13),
    means1 = c(1, 1.1, 1, 1),
    means2 = c(1.4, 1.5, 3, 2.5),
    stds1 = c(1, 2, 2, 1.3),
    stds2 = c(1, 2, 2, 1.3))
 
# display the stage results
getStageResults(design = design, dataInput = data, stage = 3)
 
# display the analysis results
getAnalysisResults(design = design, dataInput = data, equalVariances = TRUE,
    stage = stage, nPlanned = rep(10, kMax - stage),
    thetaH0 = 0, thetaH1 = 1.3, allocationRatioPlanned = 2)

Getting help

Training Courses

Please contact us and ask for more information about our training courses.

Online Help

The online documentation (help files) can be opened here: https://www.rpact.org/documentation

Additionally there are two different pdf versions of the manual available:

Vignettes

Tutorials and vignettes can be found here: Vignettes

Included Help

# load the package
library(rpact)
 
# display the manual of the package
help(package = "rpact")

Bugs and Issues

Please use our bug report form to submit bug descriptions and issues in a systematic way: https://bugreport.rpact.org

Functional Range

Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) . This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.