RPACT Package Overview

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 from CRAN

# The easiest way to get rpact is to install it from cran: 

Installation of the latest developer version

Please follow the instructions described here.

Basic Functions and Classes

Design Functions

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

Sample Size Calculation Functions

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

Dataset Function

  • getDataset()

Analysis Resuls Function

  • getAnalysisResults()

Additional Functions

  • getDesignCharacteristics()
  • getStageResults()

Plot Functions

Take a look to the appearance and graphical output of the RPACT package


Getting started

# load the package
# display the manual of the package
help(package = "rpact")
# create an inverse normal design with default parameters
design <- getDesignInverseNormal()
# take a look to the design and its default values
# display the design characteristics
# plot the design with default type 1 (Boundary Plot)
# 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 to the 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 manual can be opened here: https://manual.rpact.org/html

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

Included Help

# load the package
# 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

RPACT is a comprehensive package that enables the simulation and analysis of parallel group designs with continuous, binary, and survival endpoint.

In RPACT particularly, the methods described in the recent monograph of Wassmer and Brannath (published by Springer, 2016) are implemented.

The simulation based evaluation of operating characteristics of adaptive designs are becoming increasingly important, and the package will address this issue. We develop these simulations for the most relevant types of endpoints (continuous, binary, and survival) and will include the assessment of sample size reassessment strategies based on conditional power, of futility rules and other strategies. As adaptive strategies classical group sequential tests, combination tests (inverse normal, Fisher's combination test), and adaptive tests based conditional rejection probability (CRP) principle will be available.
A comprehensive output in form of graphs and tables is provided. Additionally, a specific methodology for survival endpoints with adaptation based on surrogates will be available.

For the analysis and execution of an adaptive trial, all methods provided by the simulation will be available. Specific results of the adaptive methodology are also available, e.g., overall confidence intervals and p-values and conditional and predictive power assessments. The R package is fully integrated in R (i.e., no "stand alone" package) such that R specific data entry, transformations, and summary statistics can be utilized.