Getting started with rpact

Friedrich Pahlke and Gernot Wassmer

2024-04-05

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, and survival 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:

1. Use the Shiny app: shiny.rpact.com
2. Use the Vignettes: www.rpact.org/vignettes
3. Book a training: www.rpact.com

Vignettes

The vignettes are hosted at www.rpact.org/vignettes and cover the following topics:

1. Defining Group Sequential Boundaries with rpact
2. Designing Group Sequential Trials with Two Groups and a Continuous Endpoint with rpact
3. Designing Group Sequential Trials with a Binary Endpoint with rpact
4. Designing Group Sequential Trials with Two Groups and a Survival Endpoint with rpact
5. Simulation-Based Design of Group Sequential Trials with a Survival Endpoint with rpact
6. An Example to Illustrate Boundary Re-Calculations during the Trial with rpact
7. Analysis of a Group Sequential Trial with a Survival Endpoint using rpact
8. Defining Accrual Time and Accrual Intensity with rpact
9. How to use R Generics with rpact
10. How to Create Admirable Plots with rpact
11. Comparing Sample Size and Power Calculation Results for a Group Sequential Trial with a Survival Endpoint: rpact vs. gsDesign
12. Supplementing and Enhancing rpact’s Graphical Capabilities with ggplot2
13. Using the Inverse Normal Combination Test for Analyzing a Trial with Continuous Endpoint and Potential Sample Size Re-Assessment with rpact
14. Planning a Trial with Binary Endpoints with rpact
15. Planning a Survival Trial with rpact
16. Simulation of a Trial with a Binary Endpoint and Unblinded Sample Size Re-Calculation with rpact
17. How to Create Summaries with rpact
18. How to Create One- and Multi-Arm Analysis Result Plots with rpact
19. How to Create One- and Multi-Arm Simulation Result Plots with rpact
20. Simulating Multi-Arm Designs with a Continuous Endpoint using rpact
21. Analysis of a Multi-Arm Design with a Binary Endpoint using rpact
22. Step-by-Step rpact Tutorial
23. Planning and Analyzing a Group-Sequential Multi-Arm Multi-Stage Design with Binary Endpoint using rpact
    
24. Two-arm Analysis for Continuous Data with Covariates from Raw Data using rpact (exclusive)
25. How to Install the Latest rpact Developer Version (exclusive)
26. Delayed Response Designs with rpact
27. 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 rpact comparison 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, lambda and median, 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.

About

• rpact is a comprehensive validated¹ 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 rpact package
  • 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

• info@rpact.com
• www.rpact.com/contact