Getting started with rpact

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

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)