Designs
Quick Start
Getting started with rpact
Confirmatory Adaptive Clinical Trial Design, Simulation, and Analysis
Functional Range
- Fixed sample design and designs with interim analysis stages
- Sample size and power calculation for
- means (continuous endpoint)
- rates (binary endpoint)
- survival trials with flexible recruitment and survival time options
- count data
- Simulation tool for means, rates, survival data, and count data
- Assessment of adaptive sample size/event number recalculations based on conditional power
- Assessment of treatment selection strategies in multi-arm trials
- Adaptive analysis of means, rates, and survival data
- Adaptive designs and analysis for multi-arm trials
- Adaptive analysis and simulation tools for enrichment design testing means, rates, and hazard ratios
- Automatic boundary recalculations during the trial for analysis with alpha spending approach, including under- and over-running
Learn to use rpact
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
Vignettes
The vignettes are hosted at www.rpact.org/vignettes and cover the following topics:
- Defining Group Sequential Boundaries with rpact
- Designing Group Sequential Trials with Two Groups and a Continuous Endpoint with rpact
- Designing Group Sequential Trials with a Binary Endpoint with rpact
- Designing Group Sequential Trials with Two Groups and a Survival Endpoint with rpact
- Simulation-Based Design of Group Sequential Trials with a Survival Endpoint with rpact
- An Example to Illustrate Boundary Re-Calculations during the Trial with rpact
- Analysis of a Group Sequential Trial with a Survival Endpoint using rpact
- Defining Accrual Time and Accrual Intensity with rpact
- How to use R Generics with rpact
- How to Create Admirable Plots with rpact
- Comparing Sample Size and Power Calculation Results for a Group Sequential Trial with a Survival Endpoint: rpact vs. gsDesign
- Supplementing and Enhancing rpact’s Graphical Capabilities with ggplot2
- Using the Inverse Normal Combination Test for Analyzing a Trial with Continuous Endpoint and Potential Sample Size Re-Assessment with rpact
- Planning a Trial with Binary Endpoints with rpact
- Planning a Survival Trial with rpact
- Simulation of a Trial with a Binary Endpoint and Unblinded Sample Size Re-Calculation with rpact
- How to Create Summaries with rpact
- How to Create One- and Multi-Arm Analysis Result Plots with rpact
- How to Create One- and Multi-Arm Simulation Result Plots with rpact
- Simulating Multi-Arm Designs with a Continuous Endpoint using rpact
- Analysis of a Multi-Arm Design with a Binary Endpoint using rpact
- Step-by-Step rpact Tutorial
- Planning and Analyzing a Group-Sequential Multi-Arm Multi-Stage Design with Binary Endpoint using rpact
- Two-arm Analysis for Continuous Data with Covariates from Raw Data using rpact (exclusive)
- How to Install the Latest rpact Developer Version (exclusive)
- Delayed Response Designs with rpact
- Sample Size Calculation for Count Data
User Concept
Workflow
- Everything is starting with a design, e.g.:
design <- getDesignGroupSequential() - Find the optimal design parameters with help of
rpactcomparison tools:getDesignSet - Calculate the required sample size, e.g.:
getSampleSizeMeans(),getPowerMeans() - Simulate specific characteristics of an adaptive design, e.g.:
getSimulationMeans() - Collect your data, import it into R and create a dataset:
data <- getDataset() - Analyze your data:
getAnalysisResults(design, data)
Focus on Usability
The most important rpact functions have intuitive names:
getDesign[GroupSequential/InverseNormal/Fisher]()getDesignCharacteristics()getSampleSize[Means/Rates/Survival/Counts]()getPower[Means/Rates/Survival/Counts]()getSimulation[MultiArm/Enrichment]``[Means/`Rates`/`Survival`]`()`getDataSet()getAnalysisResults()getStageResults()
RStudio/Eclipse: auto code completion makes it easy to use these functions.
R generics
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
Utilities
Several utility functions are available, e.g.
getAccrualTime()getPiecewiseSurvivalTime()getNumberOfSubjects()getEventProbabilities()getPiecewiseExponentialDistribution()- survival helper functions for conversion of
pi,lambdaandmedian, e.g.,getLambdaByMedian() testPackage(): installation qualification on a client computer or company server (via unit tests)
Validation
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.
RPACT Connect
Connecting you to insights, downloads, and premium support: connect.rpact.com
About
- rpact is a comprehensive validated^[The rpact validation documentation is
available exclusively for our customers and supporting companies. For more
information visit
www.rpact.com/services/sla] R package
for clinical research which
- enables the design and analysis of confirmatory adaptive group sequential designs
- is a powerful sample size calculator
- is a free of charge open-source software licensed under LGPL-3
- particularly, implements the methods described in the recent monograph by Wassmer and Brannath (2016)
For more information please visit www.rpact.org
- RPACT is a company which offers
- enterprise software development services
- technical support for the
rpactpackage - consultancy and user training for clinical research using R
- validated software solutions and R package development for clinical research
For more information please visit www.rpact.com
Contact
System Status
RPACT Cloud
A graphical user interface (GUI) for the rpact R package, designed to simplify its use through an intuitive interface.
About RPACT Cloud
RPACT Cloud is a graphical user interface (front end) for the R package rpact based on R Shiny. A shared non-exclusive demo version of RPACT Cloud is available at cloud.rpact.com. Compared to the open-source R package rpact, RPACT Cloud is closed source, requiring a license to run it on a company server.
Advantages of RPACT Cloud
- Easy Start with rpact Package: RPACT Cloud provides a user-friendly interface that simplifies the use of the rpact package, making it accessible even to those who are not familiar with R.
- Smooth Transition from Other Proprietary Software Packages: Users can seamlessly switch from other proprietary software packages to RPACT Cloud, benefiting from its comprehensive features and cost-effectiveness.
- Powerful Report Generator: The integrated report generator in RPACT Cloud offers flexible export options to various formats, including PDF, HTML, Rmd, qmd, and R. This versatility ensures that users can generate and share reports in their preferred format.
- Easy Comparison of Different Trial Design Scenarios: RPACT Cloud enables users to easily compare various trial design scenarios, helping them to make informed decisions based on detailed analyses and visualizations.
Functional Range of RPACT Cloud
- Fixed sample design and designs with interim analysis stages
- Sample size and power calculation for
- means (continuous endpoint)
- rates (binary endpoint)
- survival trials with flexible recruitment and survival time options
- Simulation tool for means, rates, survival data, and count data
- Assessment of adaptive sample size/event number recalculations based on conditional power
- Assessment of treatment selection strategies in multi-arm trials
- Adaptive designs and analysis for multi-arm trials
Version History of rpact
Comparison Tool Help
This help page appears whenever the Comparison Tool is not available.
The Comparison Tool requires at least two designs to be defined. You can achieve this in one of two ways:
1. Create Two (or More) Master Designs
- On the Start tab, click Create new design
or select a preconfigured design from the Quick Start menu. - Define your design parameters.
- Repeat step 1 and 2 to create a second (and additional) design.
These saved designs are called Master Designs.
Once you have two or more Master Designs, the Comparison Tool will become available.
2. Create Variations from a Single Master Design
- On the Home screen, click Create new design
or select a preconfigured design from the Quick Start menu. - Define your design parameters (this is your Master Design).
- Change one or more parameters (e.g., significance level alpha).
- Click the ❤ (red heart) button to mark this situation as a Variation.
- Repeat steps 3–4 until you have marked two or more Variations of the Master Design.
These Variations, either together with the master design or without it, count as designs for comparison.
Once you have at least two marked Variations (or a mix of Master Designs + Variations), the Comparison Tool will appear.
Tip:
- Master Designs and Variations live side-by-side in the design list.
- You can mix and match: e.g., one Master Design + two Variations, only two Master Designs, or only two Variations.
If you still see this help page, please verify you have defined at least two designs or variations as described above.